Personalized tailored air interface

ABSTRACT

Methods and devices utilizing artificial intelligence (AI) or machine learning (ML) for customization of a device specific air interface configuration in a wireless communication network are provided. An over the air information exchange to facilitate the training of one or more AI/ML modules involves the exchange of AI/ML capability information identifying whether a device supports AI/ML for optimization of the air interface.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/939,284 entitled “PERSONALIZED TAILORED AIRINTERFACE” filed Nov. 22, 2019, the entire contents of which isincorporated herein by reference.

FIELD

The present disclosure relates to wireless communication generally, and,in particular embodiments, to methods and apparatuses for air interfacecustomization.

BACKGROUND

An air interface is the wireless communications link between two or morecommunicating devices, such as an evolved NodeB (also commonly referredto as a NodeB, a base station, NR base station, a transmit point, aremote radio head, a communications controller, a controller, and thelike) and a user equipment (UE) (also commonly referred to as a mobilestation, a subscriber, a user, a terminal, a phone, and the like).Typically, both communicating devices need to know the air interface inorder to successfully transmit and receive a transmission.

In many wireless communication systems, the air interface definition isa one-size-fits-all concept. The components within the air interfacecannot be changed or adapted once the air interface is defined. In someimplementations, only limited parameters or modes of an air interface,such as a cyclic prefix (CP) length or multiple input multiple output(MIMO) mode, can be configured. In some modern wireless systems, aconfigurable air interface concept has been adopted to provide aframework for a more flexible air interface. It is intended to provideadaptation of different components within the air interface, and toaddress the potential requirements of future applications. Some modernwireless systems, such as fifth generation (5G) or new radio (NR)network systems, support network slicing, which is a networkarchitecture that enables the multiplexing of virtualized andindependent logical networks on the same physical networkinfrastructure. In such systems, each network slice is an isolatedend-to-end network tailored to fulfill diverse requirements requested bya particular service or application. A configurable air interface hasbeen proposed for NR networks that allows for service or slice basedoptimization of the air interface to allow the air interface to beconfigured based on a service or application that will be supported bythe air interface or the network slice over which the service orapplication will be provided.

SUMMARY

Different pairs of communicating devices (i.e., a transmission sendingdevice and a transmission receiving device) may have differenttransmission capabilities and/or transmission requirements. Thedifferent transmission capabilities and/or transmission requirementstypically cannot be met optimally by a single air interface or airinterface configuration.

The configurable air interface proposed for NR networks allows serviceor slice based optimization based on selecting from a predeterminedsubset of parameters or technologies for a predetermined subset of airinterface components. If the service and/or network slice over which theservice is provided changes, the configurations of the components of thetransmit and receive chains of the communicating devices may be changedto match a new predetermined service or slice specific air interfacecorresponding to the new service or network slice.

However for each service, the transmission condition, capability andrequirements can still be quite different for each device, which means,for example, that an air interface configuration that may be optimal fordelivering a service to one device, for an example one UE, may notnecessarily be optimal for delivering the same service to another UE.

The present disclosure provides methods and apparatuses that may be usedto implement new air interfaces for wireless communication that aretailored or personalized on a device-specific basis, for example usingartificial intelligence and/or machine learning to providedevice-specific air interface optimization. For example, embodiments ofthe present disclosure include new air interfaces that go beyond anetwork slice/service specific air interface to a personalized tailoredair interface that includes a personalized service type and apersonalized air interface setting. Thus, using artificial intelligenceand/or machine learning to optimize a device-specific air interface, canachieve a new air interface configuration to satisfy the requirement ofeach UE on an individual basis.

One broad aspect of the present disclosure provides a method in awireless communication network in which a first device transmitsinformation regarding an artificial intelligence or machine learning(AI/ML) capability of the first device to a second device over an airinterface between the first device and the second device. For example,the information regarding an AI/ML capability of the first device mayidentify whether the first device supports AI/ML for optimization of atleast one air interface component over the air interface. Thus, theexchange of AI/ML capability between two communicating devices is usedto optimize one or more air interface components to accomplishdevice-specific air interface optimization.

Another broad aspect of the present disclosure provides a method in awireless communication network in which a second device receivesinformation regarding an artificial intelligence or machine learning(AI/ML) capability of a first device over an air interface between thefirst device and the second device. For example, the informationregarding an AI/ML capability of the first device may identify whetherthe first device supports AI/ML for optimization of at least one airinterface component over the air interface. In some embodiments, thesecond device may transmit an AI/ML training request to the first devicebased at least in part on the information regarding the AI/ML capabilityof the first device. Thus, the exchange of AI/ML capability between twocommunicating devices is used to optimize one or more air interfacecomponents to accomplish device-specific air interface optimization.

Yet another broad aspect of the present disclosure provides an apparatusthat includes at least one processor and a computer readable storagemedium operatively coupled to the at least one processor. The computerreadable storage medium stores programming for execution by the at leastone processor. The programming includes instructions to transmit, fromthe apparatus, information regarding an artificial intelligence ormachine learning (AI/ML) capability of the apparatus to a network deviceover an air interface between the apparatus and the network device. Forexample, the information regarding an AI/ML capability of the apparatusmay identify whether the apparatus supports AI/ML for optimization of atleast one air interface component over the air interface.

Still another broad aspect of the present disclosure provides a networkapparatus that includes at least one processor and a computer readablestorage medium operatively coupled to the at least one processor. Thecomputer readable storage medium stores programming for execution by theat least one processor. The programming includes instructions toreceive, by the network apparatus, information regarding an artificialintelligence or machine learning (AI/ML) capability of a first deviceover an air interface between the first device and the networkapparatus. For example, the information regarding an AI/ML capability ofthe first device may identify whether the first device supports AI/MLfor optimization of at least one air interface component over the airinterface. In some embodiment, the programming further comprisesinstructions to transmit an AI/ML training request to the first devicebased at least in part on the information regarding the AI/ML capabilityof the first device.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made, by way of example, to the accompanyingdrawings which show example embodiments of the present application, andin which:

FIG. 1 is a schematic diagram of an example communication systemsuitable for implementing examples described herein;

FIGS. 2 and 3 are block diagrams illustrating example devices that mayimplement the methods and teachings according to this disclosure;

FIG. 4 is a block diagram of an example computing system that mayimplement the methods and teachings according to this disclosure;

FIG. 5 illustrates an example air interface and components thereof;

FIG. 6 is a block diagram of a transmit chain of a network device and areceive chain of a user equipment device that are configured tocommunicate over an air interface;

FIG. 7A is a block diagram of a first example of a transmit chain of anetwork device and a receive chain of a user equipment device thatinclude machine learning components enabling device-specifictailoring/customization of an air interface, in accordance with a firstembodiment of this disclosure;

FIG. 7B is a block diagram of a second example of a transmit chain of anetwork device and a receive chain of a user equipment device thatinclude machine learning components enabling device-specifictailoring/customization of an air interface, in accordance with thefirst embodiment of this disclosure;

FIG. 8A is a block diagram of a first example of a transmit chain of anetwork device and a receive chain of a user equipment device thatinclude machine learning components enabling device-specifictailoring/customization of an air interface, in accordance with a secondembodiment of this disclosure;

FIG. 8B is a block diagram of a second example of a transmit chain of anetwork device and a receive chain of a user equipment device thatinclude machine learning components enabling device-specifictailoring/customization of an air interface, in accordance with thesecond embodiment of this disclosure;

FIG. 9 is a block diagram of an example of a transmit chain of a networkdevice and a receive chain of a user equipment device that includemachine learning components enabling device-specifictailoring/customization of an air interface, in accordance with a thirdembodiment of this disclosure;

FIG. 10 is a block diagram of an example of a transmit chain of anetwork device and a receive chain of a user equipment device thatinclude machine learning components enabling device-specifictailoring/customization of an air interface, in accordance with a fourthembodiment of this disclosure;

FIG. 11 is a block diagram of an example of a transmit chain of anetwork device and a receive chain of a user equipment device thatinclude machine learning components enabling device-specifictailoring/customization of an air interface, in accordance with a fifthembodiment of this disclosure;

FIG. 12 illustrates an example of an over the air information exchangeprocedure for a training phase of machine learning components enablingdevice-specific tailoring/customization of an air interface, inaccordance with an embodiment of this disclosure;

FIG. 13 illustrates another example of an over the air informationexchange procedure for a training phase of machine learning componentsenabling device-specific tailoring/customization of an air interface, inaccordance with an embodiment of this disclosure;

FIG. 14 illustrates an example of an over the air information exchangeprocedure for a normal operations phase of machine learning componentsenabling device-specific tailoring/customization of an air interface, inaccordance with an embodiment of this disclosure; and

FIG. 15 illustrates an example of an over the air information exchangeprocedure for a re-training phase of machine learning componentsenabling device-specific tailoring/customization of an air interface, inaccordance with an embodiment of this disclosure.

Similar reference numerals may have been used in different figures todenote similar components.

DESCRIPTION OF EXAMPLE EMBODIMENTS

To assist in understanding the present disclosure, an example wirelesscommunication system is described below.

FIG. 1 illustrates an example wireless communication system 100 (alsoreferred to as wireless system 100) in which embodiments of the presentdisclosure could be implemented. In general, the wireless system 100enables multiple wireless or wired elements to communicate data andother content. The wireless system 100 may enable content (e.g., voice,data, video, text, etc.) to be communicated (e.g., via broadcast,narrowcast, user device to user device, etc.) among entities of thesystem 100. The wireless system 100 may operate by sharing resourcessuch as bandwidth. The wireless system 100 may be suitable for wirelesscommunications using 5G technology and/or later generation wirelesstechnology (e.g., 6G or later). In some examples, the wireless system100 may also accommodate some legacy wireless technology (e.g., 3G or 4Gwireless technology).

In the example shown, the wireless system 100 includes electronicdevices (ED) 110 a-110 c (generically referred to as ED 110), radioaccess networks (RANs) 120 a-120 b (generically referred to as RAN 120),a core network 130, a public switched telephone network (PSTN) 140, theinternet 150, and other networks 160. In some examples, one or more ofthe networks may be omitted or replaced by a different type of network.Other networks may be included in the wireless system 100. Althoughcertain numbers of these components or elements are shown in FIG. 1, anyreasonable number of these components or elements may be included in thewireless system 100.

The EDs 110 are configured to operate, communicate, or both, in thewireless system 100. For example, the EDs 110 may be configured totransmit, receive, or both via wireless or wired communication channels.Each ED 110 represents any suitable end user device for wirelessoperation and may include such devices (or may be referred to) as a userequipment/device (UE), a wireless transmit/receive unit (WTRU), a mobilestation, a fixed or mobile subscriber unit, a cellular telephone, astation (STA), a machine type communication (MTC) device, a personaldigital assistant (PDA), a smartphone, a laptop, a computer, a tablet, awireless sensor, or a consumer electronics device, among otherpossibilities. Future generation EDs 110 may be referred to using otherterms.

In FIG. 1, the RANs 120 include base stations (BSs) 170 a-170 b(generically referred to as BS 170), respectively. Each BS 170 isconfigured to wirelessly interface with one or more of the EDs 110 toenable access to any other BS 170, the core network 130, the PSTN 140,the internet 150, and/or the other networks 160. For example, the BS 170s may include (or be) one or more of several well-known devices, such asa base transceiver station (BTS), a radio base station, a Node-B(NodeB), an evolved NodeB (eNodeB), a Home eNodeB, a gNodeB (sometimescalled a next-generation Node B), a transmission point (TP), a transmitand receive point (TRP), a site controller, an access point (AP), or awireless router, among other possibilities. Future generation BSs 170may be referred to using other terms. Any ED 110 may be alternatively oradditionally configured to interface, access, or communicate with anyother BS 170, the internet 150, the core network 130, the PSTN 140, theother networks 160, or any combination of the preceding. The wirelesssystem 100 may include RANs, such as RAN 120 b, wherein thecorresponding BS 170 b accesses the core network 130 via the internet150, as shown.

The EDs 110 and BSs 170 are examples of communication equipment that canbe configured to implement some or all of the functionality and/orembodiments described herein. In the embodiment shown in FIG. 1, the BS170 a forms part of the RAN 120 a, which may include other BSs, basestation controller(s) (BSC), radio network controller(s) (RNC), relaynodes, elements, and/or devices. Any BS 170 may be a single element, asshown, or multiple elements, distributed in the corresponding RAN, orotherwise. Also, the BS 170 b forms part of the RAN 120 b, which mayinclude other BSs, elements, and/or devices. Each BS 170 transmitsand/or receives wireless signals within a particular geographic regionor area, sometimes referred to as a “cell” or “coverage area”. A cellmay be further divided into cell sectors, and a BS 170 may, for example,employ multiple transceivers to provide service to multiple sectors. Insome embodiments there may be established pico or femto cells where theradio access technology supports such. A macro cell may encompass one ormore smaller cells. In some embodiments, multiple transceivers could beused for each cell, for example using multiple-input multiple-output(MIMO) technology. The number of RANs 120 shown is exemplary only. Anynumber of RANs may be contemplated when devising the wireless system100.

The BSs 170 communicate with one or more of the EDs 110 over one or moreair interfaces 190 a using wireless communication links (e.g. radiofrequency (RF), microwave, infrared (IR), etc.). The EDs 110 may alsocommunicate directly with one another via one or more sidelink airinterfaces 190 b. The interfaces 190 a and 190 b may be generallyreferred to as air interfaces 190. BS-ED communications over interfaces190 a and ED-ED communications over interfaces 190 b may use similarcommunication technology. The air interfaces 190 may utilize anysuitable radio access technology. For example, the wireless system 100may implement one or more channel access methods, such as code divisionmultiple access (CDMA), time division multiple access (TDMA), frequencydivision multiple access (FDMA), orthogonal FDMA (OFDMA), orsingle-carrier FDMA (SC-FDMA) in the air interfaces 190. The airinterfaces 190 may utilize other higher dimension signal spaces, whichmay involve a combine of orthogonal and/or non-orthogonal dimensions.

The RANs 120 are in communication with the core network 130 to providethe EDs 110 with various services such as voice, data, and otherservices. The RANs 120 and/or the core network 130 may be in direct orindirect communication with one or more other RANs (not shown), whichmay or may not be directly served by core network 130, and may or maynot employ the same radio access technology as RAN 120 a, RAN 120 b orboth. The core network 130 may also serve as a gateway access between(i) the RANs 120 or EDs 110 or both, and (ii) other networks (such asthe PSTN 140, the internet 150, and the other networks 160). Inaddition, some or all of the EDs 110 may include functionality forcommunicating with different wireless networks over different wirelesslinks using different wireless technologies and/or protocols. Instead ofwireless communication (or in addition thereto), the EDs 110 maycommunicate via wired communication channels to a service provider orswitch (not shown), and to the internet 150. PSTN 140 may includecircuit switched telephone networks for providing plain old telephoneservice (POTS). Internet 150 may include a network of computers andsubnets (intranets) or both, and incorporate protocols, such as InternetProtocol (IP), Transmission Control Protocol (TCP), User DatagramProtocol (UDP). EDs 110 may be multimode devices capable of operationaccording to multiple radio access technologies, and incorporatemultiple transceivers necessary to support such.

FIGS. 2 and 3 illustrate example devices that may implement the methodsand teachings according to this disclosure. In particular, FIG. 2illustrates an example ED 110, and FIG. 3 illustrates an example basestation 170. These components could be used in the communication system100 or in any other suitable system.

As shown in FIG. 2, the ED 110 includes at least one processing unit200. The processing unit 200 implements various processing operations ofthe ED 110. For example, the processing unit 200 could perform signalcoding, data processing, power control, input/output processing, or anyother functionality enabling the ED 110 to operate in the communicationsystem 100. The processing unit 200 may also be configured to implementsome or all of the functionality and/or embodiments described in moredetail elsewhere herein. Each processing unit 200 includes any suitableprocessing or computing device configured to perform one or moreoperations. Each processing unit 200 could, for example, include amicroprocessor, microcontroller, digital signal processor, fieldprogrammable gate array, or application specific integrated circuit.

The ED 110 also includes at least one transceiver 202. The transceiver202 is configured to modulate data or other content for transmission byat least one antenna or Network Interface Controller (NIC) 204. Thetransceiver 202 is also configured to demodulate data or other contentreceived by the at least one antenna 204. Each transceiver 202 includesany suitable structure for generating signals for wireless or wiredtransmission and/or processing signals received wirelessly or by wire.Each antenna 204 includes any suitable structure for transmitting and/orreceiving wireless or wired signals. One or multiple transceivers 202could be used in the ED 110. One or multiple antennas 204 could be usedin the ED 110. Although shown as a single functional unit, a transceiver202 could also be implemented using at least one transmitter and atleast one separate receiver.

The ED 110 further includes one or more input/output devices 206 orinterfaces (such as a wired interface to the internet 150 in FIG. 1).The input/output devices 206 permit interaction with a user or otherdevices in the network. Each input/output device 206 includes anysuitable structure for providing information to or receiving informationfrom a user, such as a speaker, microphone, keypad, keyboard, display,or touch screen, including network interface communications.

In addition, the ED 110 includes at least one memory 208. The memory 208stores instructions and data used, generated, or collected by the ED110. For example, the memory 208 could store software instructions ormodules configured to implement some or all of the functionality and/orembodiments described herein and that are executed by the processingunit(s) 200. Each memory 208 includes any suitable volatile and/ornon-volatile storage and retrieval device(s). Any suitable type ofmemory may be used, such as random access memory (RAM), read only memory(ROM), hard disk, optical disc, subscriber identity module (SIM) card,memory stick, secure digital (SD) memory card, and the like.

As shown in FIG. 3, the base station 170 includes at least oneprocessing unit 1350, at least one transmitter 252, at least onereceiver 254, one or more antennas 256, at least one memory 258, and oneor more input/output devices or interfaces 266. A transceiver, notshown, may be used instead of the transmitter 252 and receiver 254. Ascheduler 253 may be coupled to the processing unit 250. The scheduler253 may be included within or operated separately from the base station170. The processing unit 250 implements various processing operations ofthe base station 170, such as signal coding, data processing, powercontrol, input/output processing, or any other functionality. Theprocessing unit 250 can also be configured to implement some or all ofthe functionality and/or embodiments described in more detail herein.Each processing unit 250 includes any suitable processing or computingdevice configured to perform one or more operations. Each processingunit 250 could, for example, include a microprocessor, microcontroller,digital signal processor, field programmable gate array, or applicationspecific integrated circuit.

Each transmitter 252 includes any suitable structure for generatingsignals for wireless or wired transmission to one or more EDs or otherdevices. Each receiver 254 includes any suitable structure forprocessing signals received wirelessly or by wire from one or more EDsor other devices. Although shown as separate components, at least onetransmitter 252 and at least one receiver 254 could be combined into atransceiver. Each antenna 256 includes any suitable structure fortransmitting and/or receiving wireless or wired signals. Although acommon antenna 256 is shown here as being coupled to both thetransmitter 252 and the receiver 254, one or more antennas 256 could becoupled to the transmitter(s) 252, and one or more separate antennas 256could be coupled to the receiver(s) 254. Each memory 258 includes anysuitable volatile and/or non-volatile storage and retrieval device(s)such as those described above in connection to the ED 110 in FIG. 2. Thememory 258 stores instructions and data used, generated, or collected bythe base station 170. For example, the memory 258 could store softwareinstructions or modules configured to implement some or all of thefunctionality and/or embodiments described herein and that are executedby the processing unit(s) 250.

Each input/output device 266 permits interaction with a user or otherdevices in the network. Each input/output device 266 includes anysuitable structure for providing information to or receiving/providinginformation from a user, including network interface communications.

It should be appreciated that one or more steps of the embodimentmethods provided herein may be performed by corresponding units ormodules, according to FIG. 4. For example, a signal may be transmittedby a transmitting unit or a transmitting module. A signal may bereceived by a receiving unit or a receiving module. A signal may beprocessed by a processing unit or a processing module. Other steps maybe performed by an artificial intelligence (AI) or machine learning (ML)module. The respective units/modules may be implemented using hardware,one or more components or devices that execute software, or acombination thereof. For instance, one or more of the units/modules maybe an integrated circuit, such as field programmable gate arrays (FPGAs)or application-specific integrated circuits (ASICs). It will beappreciated that where the modules are implemented using software forexecution by a processor for example, they may be retrieved by aprocessor, in whole or part as needed, individually or together forprocessing, in single or multiple instances, and that the modulesthemselves may include instructions for further deployment andinstantiation.

Additional details regarding the EDs such as 110 and base stations suchas 170 are known to those of skill in the art. As such, these detailsare omitted here.

Referring back to FIG. 1, different pairs of communicating devices(i.e., a transmission sending device and a transmission receivingdevice), such as ED 110 a communicating with BS 170 a or ED 110 bcommunicating with BS 170 a, may have different transmissioncapabilities and/or transmission requirements. The differenttransmission capabilities and/or transmission requirements typicallycannot be met optimally by a single air interface or air interfaceconfiguration.

As discussed above, a configurable air interface has been proposed toaddress this issue. FIG. 5 illustrates a diagram of an example of aconfigurable air interface 300. Air interface 300 comprises a number ofbuilding blocks that collectively specify how a transmission is to bemade and/or received. The building blocks of air interface 300 mayinclude waveform building block 305, frame structure building block 310,multiple access scheme building block 315, a protocols building block320, a coding and modulation building block 325, and an antenna arrayprocessing building block 330.

Frame structure building block 310 may specify a configuration of aframe or group of frames. Non-limiting examples of frame structureoptions include a configurable multi-level transmission time interval(TTI), a fixed TTI, a configurable single-level TTI, a co-existenceconfiguration, or configurable slot, mini slot, or configurable symbolduration block (SDB) and the like. The lengths of a TTI, slot, mini slotor SDB may also be specified. Frame structure building block 310 mayalso or instead specify timing parameters for DL and/or UL transmission,such as a transmission period for DL and/or UL, and/or a time switch gapbetween DL and UL transmissions. The frame structure can be for variousduplexing schemes, such as time domain duplexing (TDD), frequencydivision duplexing (FDD) and full duplex operation.

Multiple access scheme building block 315 may specify how access to achannel is scheduled or configured for one or more users. Non-limitingexamples of multiple access technique options include scheduled access,grant-free access, dedicated channel resource (no sharing betweenmultiple users), contention based shared channel resource,non-contention based shared channel resource, cognitive radio basedaccess, and the like.

Protocols building block 320 may specify how a transmission and/or are-transmission are to be made. Non-limiting examples of transmissionand/or re-transmission mechanism options include those that specify ascheduled data pipe size, a signaling mechanism for transmission and/orre-transmission, a re-transmission mechanism, and the like.

Coding and modulation building block 325 may specify how informationbeing transmitted may be encoded (decoded) and modulated (demodulated)for transmission (reception) purposes. Non-limiting examples of codingand/or modulation technique options include low density parity check(LDPC) codes, polar codes, turbo trellis codes, turbo product codes,fountain codes, rateless codes, network codes, binary phase shift keying(BPSK), π/2-BPSK, quadrature phase shift keying (QPSK), quadratureamplitude modulation (QAM) such as 16QAM, 64QAM, 256QAM, hierarchicalmodulation, low PAPR modulation, non-linear modulation non-QAM basedmodulation, and the like.

Waveform building block 305 may specify a shape and form of a signalbeing transmitted. Non-limiting examples of waveform options includeOrthogonal Frequency Division Multiplexing (OFDM) based waveform such asfiltered OFDM (f-OFDM), Wavelet Packet Modulation (WPM), Faster ThanNyquist (FTN) Waveform, low Peak to Average Ratio Waveform (low PAPR WFsuch as DFT spread OFDM waveform), Filter Bank Multicarrier (FBMC)Waveform, Single Carrier Frequency Division Multiple Access (SC-FDMA),and the like. For OFDM-based waveforms, the waveform building block 305may specify the associated waveform parameters such as sub-carrierspacings and cyclic prefix (CP) overhead.

Antenna array processing building block 330 may specify parameters forantenna array signal processing for channel acquisition andprecoding/beamforming generation. In some embodiments, the functionalityof the waveform building block 305 and the antenna array processingbuilding block 330 may be combined as a multiple antenna waveformgenerator block.

Since the air interface 300 comprises a plurality of building blocks,and each building block may have a plurality of candidate technologies,it may be possible to configure a large number of different airinterface profiles, where each air interface profile defines arespective air interface configuration option.

For example, the configurable air interface proposed for new radio (NR)networks allows service or slice based optimization, which can beadvantageous because the potential application requirements for airinterface technologies can be complex and diverse. Similar to the airinterface 300 shown in FIG. 3, the configurable air interface proposedfor 5G networks supports adaptive waveform, adaptive protocols, adaptiveframe structure, adaptive coding and modulation family and adaptivemultiple access schemes. With such mechanisms, the air interface canpotentially accommodate a wide variety of user services, spectrum bandsand traffic levels.

FIG. 6 illustrates an example of components in a transmit chain 400 of abase station 170 and components of a receive chain 450 of a UE 110 thatmay be configurable as part of a configurable air interface to allow thebase station 170 and the UE 110 to communicate.

The components of the transmit chain 400 of the base station 170 includea source encoder 402, a channel encoder 404 and a modulator 406. Sourceencoder 402, channel encoder 404 and modulator 406 may each beimplemented as a specific hardware block, or may be implemented in partas software modules executing in a processor, such as a microprocessor,a digital signal processor, a custom application specific integratedcircuit, or a custom compiled logic array of a field programmable logicarray.

The components of the receive chain 450 of the UE 110 include ademodulator 452 and a channel decoder 454. Demodulator 452 and channeldecoder 454 may each be implemented as a specific hardware block, or maybe implemented in part as software modules executing in a processor,such as a microprocessor, a digital signal processor, a customapplication specific integrated circuit, or a custom compiled logicarray of a field programmable logic array.

In operation, source encoder 402 encodes uncompressed raw data togenerate compressed information bits, which are in turn encoded bychannel encoder to generate channel coded information bits, which arethen modulated by modulator 406 to generate modulated signals. In thisexample, the modulation performed by modulator 406 includes quadratureamplitude modulation (QAM) mapping and waveform generation. Themodulated signals generated by modulator 406 are transmitted from basestation 170 to UE 110 over one or more wireless channels. A base stationcan have multiple transmit antennas, in which case a waveform may begenerated for each of the antennas. In such cases, the generatedwaveforms may contain different contents for each of the multipletransmit antennas, e.g., in a MIMO mode transmission. At UE 110, thereceived signals from base station 170 are demodulated by demodulator452 to generate demodulated signals. A UE can have multiple receiveantennas, in which case demodulator 452 may be configured to processwaveforms received from multiple receive antennas as part of thewaveform recovery process. The demodulated signals generated bydemodulator 452 are decoded by channel decoder 454 to generate recoveredcompressed information bits. Source decoder 456 decodes the recoveredcompressed information bits to generate recovered uncompressed raw data.

Waveform here in the embodiment of FIG. 4 or the following embodiments,may specify a shape and form of a signal being transmitted. Non-limitingexamples of waveform options include Orthogonal Frequency DivisionMultiplexing (OFDM) based waveform such as filtered OFDM (f-OFDM),Wavelet Packet Modulation (WPM), Faster Than Nyquist (FTN) Waveform, lowPeak to Average Ratio Waveform (low PAPR WF such as DFT spread OFDMwaveform), Filter Bank Multicarrier (FBMC) Waveform, Single CarrierFrequency Division Multiple Access (SC-FDMA), and the like. ForOFDM-based waveforms, the waveform may specify the associated waveformparameters such as sub-carrier spacings and cyclic prefix (CP) overhead.

The coding and modulation performed by the components of the transmitchain 400 and the corresponding demodulation and decoding performed bythe components of the receive chain 450 may be configured according to amodulation and coding scheme (MCS) corresponding to a service or slicespecific air interface in order to support delivery of a service orapplication to UE 110 according to the selected code scheme andmodulation scheme. If the service and/or network slice over which theservice is provided changes, the configurations of the components of thetransmit and receive chains of the base station 170 and UE 110 may bechanged to match a new predetermined service or slice specific airinterface corresponding to the new service or network slice. As notedabove, a service or slice specific air interface such as this, which isbased on selecting from a predetermined subset of parameters ortechnologies for a predetermined subset of air interface components, canpotentially accommodate a wide variety of user services, spectrum bandsand traffic levels.

However for each service, the transmission condition and requirementscan still be quite different for each UE/device, which means, forexample, that an air interface configuration that may be optimal fordelivering a service to one UE/device may not necessarily be optimal fordelivering the same service to another UE. Therefore, it would bedesirable to provide further optimization of a UE/device specific airinterface configuration.

Machine learning (ML) and artificial intelligence (AI) approaches havebeen used for solving many difficult and complex problems. To assist inunderstanding the present disclosure, some background discussion of MLand AI is now provided. AI is an emerging and fast-growing field thanksto the advances made in the field of computer architecture and inparticular general purpose graphics processing units (GP-GPUs). A neuralnetwork, which is a form of ML, may be considered as a type of fittingfunction. Deep learning is one realization of a neural network, whichcontains more than one interconnected layer of artificial neurons. Totrain a deep neural network to fit a function (e.g., training using agreat amount of input samples and output samples), the weight andthreshold of each neuron are updated iteratively, so as to minimize anoverall loss function or maximize an overall reward function. Theiteration may be achieved by a gradient-descent or ascentback-propagation algorithm over training samples, which may require thatthe deep neural network architecture and the loss or reward function bemathematically differentiable.

Trainability typically requires: a function set (the neural networkarchitecture) that defines an exploration space boundary within which agradient-descent algorithm may traverse; and one or more loss (orreward) function(s) being differentiable with respect to each neuron'scoefficient (for gradient-ascent or descent training) on that neuralnetwork architecture.

A deep neural network is often used for performing feature capture, andfor performing prediction. Feature capture serves to extract usefulinformation from a number of complex data, and this may be considered aform of dimension reduction. Prediction involves interpolation orextrapolation, to generate new data (generally referred to as predictedor estimated data) from sample data. Both these tasks may assume thatthe input data possess an intrinsic autoregression characteristic. Forexample, a pixel of an image usually has some relationship with itsneighboring pixels. A convolutional neural network (CNN) may bedeveloped to use this relationship to reduce the dimension of the data.

The present disclosure describes examples that may be used to implementnew air interfaces for wireless communication that are tailored orpersonalized on a device-specific basis using AI/ML to providedevice-specific air interface optimization. For example, embodiments ofthe present disclosure include new air interfaces that go beyond anetwork slice/service specific air interface to a personalized tailoredair interface that includes a personalized service type and apersonalized air interface setting. Examples of such personalized airinterface settings may include one or more of the following: customizedcode scheme and modulation scheme; customized transmission scheme suchas MIMO beamforming (BF), including channel acquisition/reconstructionand precoding; customized waveform type and associated parameters suchas customized pulse shapes and parameters such as roll-off factors of anRRC pulse; customized frame structure; customizedtransmission/retransmission scheme and associated parameters such asproduct-code or inter-codebook or inter-TB 2D joint coding basedretransmission and parameters such as incremental parity bit size andinterleavers used; UE cooperation based retransmission and/or customizedtransmit-receive point (TRP) layer/type.

In some embodiments, the personalized tailored air interface parametersmay be determined using AI/ML based on the physical speed/velocity atwhich the device is moving, a link budget of the device, the channelconditions of the device, one or more device capabilities and/or aservice type that is to be supported. In some embodiments, the servicetype itself can be customized with UE-specific service parameters, suchas quality of service (QoS) requirement(s), traffic pattern, etc.

In some embodiments, the personalized tailored air interface parametersmay be optimized on the fly with minimal signaling overhead. Forexample, for 5G network implementations, the parameters may beconfigured from predefined candidate parameter sets. For next generationnetwork implementations, e.g., for sixth generation (6G) networks, theparameters maybe adapted in a more flexible manner with real time ornear real time optimization.

As will be discussed later, the level or type of air interfaceoptimization available to a device may depend on the AI/ML capability ofthe device. If a user equipment has some AI/ML capability, the UE canwork together with network device(s) to optimize its air interface(i.e., both sides of the air interface apply AI/ML to optimize the airinterface). A UE that has no AI/ML capability may still help a networkdevice to optimize an air interface during a training phase and/orduring a normal operation phase by providing some types of measurementresults to the network device for use in training AI/ML component(s) ofthe network device. For example, a high end AI/ML capable device may beable to benefit from full scale self-optimization of each component ofan air interface (e.g., optimization of coding, modulation and waveformgeneration, MIMO operation optimization). A lower end AI/ML capabledevice may only be able to benefit from partial self-optimization ofless than all components of an air interface. In some cases, a devicemay be dependent on centralized learning/training (e.g., all learning isdone centrally in the network, such as at a base station). In othercases, learning/training may be based on federated learning, which is amachine learning technique that trains an algorithm across multipledecentralized edge devices or servers holding local data samples,without exchanging their data samples. In still other cases,learning/training may also or instead involve device cooperativelearning.

As discussed above, an air interface generally includes a number ofcomponents and associated parameters that collectively specify how atransmission is to be made and/or received over a wirelesscommunications link between two or more communicating devices. Forexample, an air interface may include one or more components definingthe waveform(s), frame structure(s), multiple access scheme(s),protocol(s), coding scheme(s) and/or modulation scheme(s) for conveyingdata over a wireless communications link. The methods and devicesdisclosed herein provide a mechanism of AI/ML enabled/assisted airinterface personalized optimization that supports different levels ofper-UE/device based optimization. The disclosed examples also provideover the air signaling mechanisms to support per-UE/device based airinterface function optimization.

FIG. 7A illustrates a first example of a transmit chain 500 of a basestation 170 and a receive chain 550 of a UE 110 that each include anAI/ML module 502,552 that is trainable in order to provide a tailoredpersonalized air interface between the base station 170 and UE 110, inaccordance with an embodiment of the present disclosure. AI/MLcomponents as referenced herein are intended to be modules or blocksbased on an implementation of ML mechanisms. One example of an MLimplementation is a neural network implemented in hardware, one or morecomponents that execute software, or a combination thereof.

The AI/ML module 502 of the base station 170 includes a joint source andchannel encoder component 504, a modulator component 506 and a waveformgenerator component 508.

The AI/ML module 552 of the UE 110 includes a joint waveform recovery,demodulator and source and channel decoder component 554.

The AI/ML module 502 provides AI/ML based autonomous optimization of allbasic baseband signal processing functions including channel coding (orsource coding plus channel coding) via encoding component 504,modulation via modulation component 506 and waveform generation viawaveform generator 508. The base station 170 may have multiple transmitantennas, and in such embodiments the waveform generator 508 may beconfigured to generate a waveform for each of the transmit antennas. TheAI/ML module 552 at the UE 110 provides the reciprocal based bandprocessing functionality in order to recover information bits/raw datafrom signals received from the base station 170. The UE 110 may havemultiple receive antennas, and in such embodiments the AI/ML module 552may be configured to process waveforms received from multiple receiveantennas as part of the waveform recovery process.

The coding, modulation and waveform generation may be optimizedindividually or two or more may be jointly optimized.

Several options are possible for individual optimization of the variouscomponents of the AI/ML modules 502, 552. Some non-limiting examples ofthese options are described below.

For example, for individual optimization of channel coding without apredefined coding scheme and parameters, self-learning/training andoptimization may be used to determine an optimal coding scheme andparameters. For example, in some embodiments, a forward error correction(FEC) scheme is not predefined and AI/ML is used to determine a UEspecific customized FEC scheme. In such embodiments, autoencoder basedML may be used as part of an iterative training process during atraining phase in order to train an encoder component at a transmittingdevice and a decoder component at a receiving device. For example,during such a training process, an encoder at a base station and adecoder at a UE may be iteratively trained by exchanging a trainingsequence/updated training sequence. In general, the more trainedcases/scenarios, the better performance. After training is done, thetrained encoder component at the transmitting device and the traineddecoder component at the receiving device can work together based onchanging channel conditions to provide encoded data that may outperformresults generated from a non-AI/ML based FEC scheme. In someembodiments, the AI/ML algorithms for self-learning/training andoptimization may be downloaded by the UE from a network/server/otherdevice.

For individual optimization of channel coding with predefined codingschemes, such as low density parity check (LDPC) code, Reed-Muller (RM)code, polar code or other coding scheme, the parameters for the codingscheme can be optimized.

The parameters for channel coding can be signaled to UE from time totime (periodically or event triggered), e.g., via radio resource control(RRC) signaling or dynamically through downlink control information(DCI) in a dynamic downlink control channel or the combination of theRRC signaling and DCI, or group DCI, or other new physical layersignaling. Training can be done all on the network side or assisted byUE side training or mutual training between the network side and the UEside.

In the example illustrated in FIG. 7A, the input to AI/ML module 502 isuncompressed raw data and source coding and channel coding are donejointly by AI/ML component 504. An alternative example is illustrated inFIG. 7B, in which source coding is done separately by a source encoder501 to generate compressed information bits that are then received byAI/ML module 502 where they are channel coded by AI/ML component 504.Similarly, in the example illustrated in FIG. 7B, the output of theAI/ML module 552 at the UE 110 is recovered compressed information bitsthat are then decoded by a source decoder 555 to generate recovered rawdata, whereas the AI/ML module 552 in FIG. 7A outputs recovered rawdata.

For individual optimization of modulation without a predefinedconstellation, modulation may be done by an AI/ML module, theoptimization targets and or algorithms of which (e.g., the AI/MLcomponent 506) are understood by both the transmitter and the receiver(e.g., the base station 170 and UE 110, respectively, in the examplescenario shown in FIG. 7A). For example, the AI/ML algorithm may beconfigured to maximize Euclidian or non-Euclidian distance betweenconstellation points.

For individual optimization of modulation with a predefined non-linearmodulator, the parameters for the modulation may be done byself-optimization.

For individual optimization of waveform generation without a predefinedwaveform type, without a predefined pulse shape and without predefinedwaveform parameters, self-learning/training and optimization may be usedto determine optimal waveform type, pulse shape and waveform parameters.In some embodiments, the AI/ML algorithms for self-learning/training andoptimization may be downloaded by the UE from a network/server/otherdevice.

In some embodiments, there may be a finite set of predefined waveformtypes, and selection of a predefined waveform type from the finite setand determination of the pulse shape and other waveform parameters maybe done through self-optimization.

Several options are also possible for joint optimization of two or moreof the components of the AI/ML modules 502, 552. Some non-limitingexamples of these options are described below.

For example, in some embodiments, the coding via component 504 (channelcoding or joint source and channel coding) and the modulationimplemented via component 506 may be jointly optimized with AI/ML, andthe waveform generation via component 508 may be optimized separately.Multi-dimensional modulation, which is conceptually similar totrellis-coded modulation, is one example of a combined coding andmodulation scheme that may be used in some embodiments of the presentdisclosure. For example, in some embodiments, AI/ML may be used tocreate a customized multi-dimensional modulation scheme for a pair ofcommunicating devices, e.g., a base station and a UE.

In other embodiments, the modulation via component 504 and the waveformgeneration via component 508 may be jointly optimized with AI/ML, andthe coding via component 504 may be optimized separately. In otherembodiments, the coding, modulation and waveform generation may all bejointly optimized with AI/ML.

FIGS. 8A and 8B illustrate examples of a transmit chain 600 of a basestation 170 and a receive chain 650 of a UE 110 that each include anAI/ML module 602,652 that is trainable in order to realize UE specificoptimization and/or provide a tailored or personalized air interfacebetween the base station 170 and UE 110, in accordance with a secondembodiment of the present disclosure. In the example shown in FIG. 8A,the transmit chain 600 of base station 170 includes an AI/ML module 602and a waveform generator 605. AI/ML module 602 of the base station 170includes a joint source and channel encoder and modulation component604. Similarly, in this example the receive chain 650 of UE 110 includesa waveform processor 651 and an AI/ML module 652, which includes a jointdemodulator and source and channel decoder component 654.

Unlike the examples shown in FIGS. 7A and 7B, in which the AI/ML modules502,552 provide AI/ML based autonomous optimization of all basicbaseband signal processing functions including coding/decoding,modulation/demodulation and waveform generation/processing, in theexample shown in FIG. 8A the AI/ML module 602 provides AI/ML basedautonomous optimization of coding and modulation via component 604, andnon-AI/ML based waveform generation is managed independently viawaveform generator 605. The base station 170 may have multiple transmitantennas, and in such embodiments the waveform generator 605 may beconfigured to generate a waveform for each of the transmit antennas. TheAI/ML module 652 at the UE 110 provides the reciprocal optimizedbaseband processing functionality on modulated signals recovered bywaveform processor 651. The UE 110 may have multiple receive antennas,and in such embodiments the waveform processor 651 may be configured toprocess waveforms received from multiple receive antennas as part of thewaveform recovery process.

In the example illustrated in FIG. 8A, the input to AI/ML module 602 isuncompressed raw data and joint source and channel coding and modulationare done by AI/ML component 604. The example illustrated in FIG. 8Bdiffers from the example illustrated in FIG. 8A in that in FIG. 8Asource coding is done separately by a source encoder 601 to generateinformation bits that are then received by AI/ML module 602 where theyare jointly channel coded and modulated by AI/ML component 604.Similarly, in the example illustrated in FIG. 8B, the output of theAI/ML module 652 at the UE 110 is recovered compressed information bitsthat are then decoded by a source decoder 655 to generate recovered rawdata, whereas the AI/ML module 652 in FIG. 8A outputs recovered rawdata.

In the examples shown in FIGS. 8A and 8B, training of the AI/ML modules602 and 652 may be done by self-learning/training optimization. Codingand modulation may be optimized by AI/ML separately or jointly, asdiscussed earlier.

As mentioned above, in the examples shown in FIGS. 8A and 8B, waveformgeneration via waveform generator 605 at base station 170 and waveformprocessing via waveform processor 651 at UE 110, may be managed withoutAI/ML. For example, waveform types and waveform parameters may bepredefined and a waveform may be selected from a predefined set ofcandidate waveforms according to transmission requirements, such as peakto average power ratio (PAPR), frequency band, frequency localization,and the like. Alternatively, the waveform type and waveform parametersmay be dynamically signaled to a UE via for example downlink controlinformation (DCI) or radio resource control (RRC) signaling. In someembodiments, the predefined set of candidate waveforms may includesingle-carrier waveform and multi-carrier waveforms. Furthermore, thepredefined set of candidate waveforms may include multiple candidatewaveforms that differ in terms of one or more parameters. For example,there may be multiple candidate single-carrier waveforms predefined,such as single carrier offset QAM (OQAM) waveforms, with root-raisedcosine pulse, and predefined roll-off factors.

FIG. 9 illustrates an example of a transmit chain 700 of a base station170 and a receive chain 750 of a UE 110 that each include an AI/MLmodule 702,752 that is trainable in order to provide a tailoredpersonalized air interface between the base station 170 and UE 110, inaccordance with a third embodiment of the present disclosure.

In the example shown in FIG. 9, the transmit chain 700 of base station170 includes a source encoder 701, a channel encoder 703 and an AI/MLmodule 702 that includes a modulation component 704 and a waveformgenerator component 706. In this example the receive chain 750 of UE 110includes an AI/ML module 752, which includes a waveform processorcomponent 756 and a demodulator component 754, a channel decoder 755 anda source decoder 757.

Unlike the previous examples shown in FIGS. 7A, 7B, 8A and 8B, theexample shown in FIG. 9 utilizes non-AI/ML based source and channelcoding/decoding and AI/ML based modulation/demodulation and waveformgeneration/processing. At UE 110, the waveform processor component 756and the demodulator component 754 of the AI/ML module 652 provide thereciprocal optimized modulated signal recovery and demodulationfunctionality to recover modulated information bits. The recoveredmodulated information bits are decoded by channel decoder 755 togenerate recovered compressed information bits, which are in turndecoded by source decoder 757 to generate recovered raw data.

In the example shown in FIG. 9, training of the AI/ML modules 602 and652 may be done by self-learning/training optimization. Modulation andwaveform generation may be optimized by AI/ML separately or jointly, asdiscussed earlier. As mentioned above, in the example shown in FIG. 9,source and channel coding via source encoder 701 and channel encoder 703at base station 170 and channel and source decoding via channel decoder755 and source decoder 757 at UE 110, may be managed without AI/ML. Forexample, coding schemes and associated parameters may be predefined anda coding scheme may be selected from a predefined set of coding schemesaccording to a transmission requirement. Alternatively, the codingscheme and associated parameters may be dynamically signaled to a UE viafor example downlink control information (DCI) or radio resource control(RRC) signaling.

FIG. 10 illustrates an example of a transmit chain 800 of a base station170 and a receive chain 850 of a UE 110 that each include an AI/MLmodule 802,852 that is trainable in order to provide a tailoredpersonalized air interface between the base station 170 and UE 110, inaccordance with a fourth embodiment of the present disclosure.

In the example shown in FIG. 10, the transmit chain 800 of base station170 includes a source encoder 801, a channel encoder 803, an AI/MLmodule 802, which includes a modulation component 804, and a waveformgenerator 805. In this example the receive chain 850 of UE 110 includesa waveform processor 851, an AI/ML module 852, a channel decoder 855 anda source decoder 857. The AI/ML module 852 includes a demodulatorcomponent 854.

Unlike the previous examples, the example shown in FIG. 10 utilizesnon-AI/ML based channel coding/decoding and waveformgeneration/processing and AI/ML based modulation/demodulation. At UE110, the waveform processor 851, channel decoder 855 and source decoder857 provide non AI/ML based signal recover, channel decoding and sourcedecoding, respectively, and the demodulator component 854 of the AI/MLmodule 852 provides optimized demodulation functionality that is thereciprocal of the modulation functionality performed by the modulationcomponent 804 at base station 170.

For optimization of modulation without a predefined constellation, anAI/ML algorithm implemented by modulation component 804 may beconfigured to maximize Euclidian or non-Euclidian distance betweenconstellation points.

For optimization of modulation with a predefined non-linear modulator,the parameters for the modulation may be done by self-optimization,e.g., to optimize the distance of modulated symbols. In some scenarios,non-AI/ML based optimization of modulation may also or instead beutilized. As mentioned above, in the example shown in FIG. 10, sourceand channel coding via source encoder 801 and channel encoder 803 andwaveform generation via waveform generator 805 at base station 170 andwaveform processing via waveform processor 851 and channel and sourcedecoding via channel decoder 855 and source decoder 857 at UE 110, maybe managed without AI/ML. For example, waveform types and associatedparameters as well as coding schemes and associated parameters may bepredefined and a waveform type and a coding scheme may be selected frompredefined sets according to a transmission requirement, as discussedpreviously. Alternatively, the coding scheme and associated parametersand/or the waveform type and waveform parameters may be dynamicallysignaled to a UE via for example downlink control information (DCI) orradio resource control (RRC) signaling.

FIG. 11 illustrates an example of a transmit chain 900 of a base station170 and a receive chain 950 of a UE 110 that each include an AI/MLmodule 902,952 that is trainable in order to provide a tailoredpersonalized air interface between the base station 170 and UE 110, inaccordance with a fifth embodiment of the present disclosure.

In the example shown in FIG. 11, the transmit chain 900 of base station170 includes a source encoder 901, a channel encoder 903, a QAM mappingcomponent 905 and an AI/ML module 902 that includes a waveformgeneration component 904. In this example the receive chain 950 of UE110 includes an AI/ML module 952, a QAM demapping component 953, achannel decoder 955 and a source decoder 957. The AI/ML module 952includes a waveform processing component 954.

Unlike the previous examples, the example shown in FIG. 11 utilizesnon-AI/ML based source and channel coding/decoding andmodulation/demodulation and AI/ML based or assisted waveform generation.The AI/ML based or assisted waveform generation may enable per UE basedoptimization of one or more waveform parameters, such as pulse shape,pulse width, subcarrier spacing (SCS), cyclic prefix, pulse separation,sampling rate, PAPR and the like.

For optimization of waveform generation without a predefined waveformtype, without a predefined pulse shape and without predefined waveformparameters, self-learning/training and optimization may be used todetermine optimal waveform type, pulse shape and waveform parameters. Insome embodiments, the AI/ML algorithms for self-learning/training andoptimization may be downloaded by the UE from a network/server/otherdevice. In some embodiments, there may be a finite set of predefinedwaveform types, and selection of a predefined waveform type from thefinite set and determination of the pulse shape and other waveformparameters may be done through self-optimization. In some scenarios,non-AI/ML based optimization of waveform generation may also or insteadbe utilized.

As mentioned above, in the example shown in FIG. 11, source and channelcoding via source encoder 901 and channel encoder 903 and modulation viaQAM mapping component 905 at base station 170 and demodulation via QAMdemapping component 953 and channel and source decoding via channeldecoder 955 and source decoder 957 at UE 110, may be managed withoutAI/ML. For example, a modulation and coding scheme and associatedparameters may be selected from a predefined set of modulation andcoding schemes according to a transmission requirement, as discussedpreviously. Alternatively, the modulation and coding scheme andassociated parameters may be dynamically signaled to a UE via forexample downlink control information (DCI) or radio resource control(RRC) signaling.

Examples of over the air information exchange procedures that mayfacilitate training of ML components of communicating devices, such asvarious ML components of the base stations 170 and UEs 110 of theexamples shown in FIGS. 7 to 11 will now be described with reference toFIGS. 12 to 14.

FIG. 12 is a signal flow diagram 1000 of an example of an over the airinformation exchange procedure for a training phase of machine learningcomponents enabling device-specific tailoring/customization of an airinterface, in accordance with an embodiment of this disclosure.

In the signal flow diagram 1000, a UE and a BS or other network deviceare involved in an information exchange for an AI/ML training phase1150. Although only one UE and one BS are shown in FIG. 12 to avoidcongestion in the drawing, data collection or information sharing duringtraining, and similarly operation of a communication network, areexpected to involve more than one UE and more than one BS. For example,in some embodiments training may be done with the joint efforts frommultiple network devices and multiple UEs and air interface optimizationmay be done on a per UE basis.

The information exchange procedure begins with UE sending informationindicating an AI/ML capability of the UE to the BS at 1010. Theinformation indicating an AI/ML capability of the UE may indicatewhether or not the UE supports AI/ML for optimization of an airinterface. If the UE is capable of supporting AI/ML optimization, theinformation may also or instead indicate what type and/or level ofcomplexity of AI/ML the UE is capable of supporting, e.g., whichfunction/operation AI/ML can be supported, what kind of AI/ML algorithmcan be supported (for example, autoencoder, reinforcement learning,neural network (NN), deep neural network (DNN), how many layers of NNcan be supported, etc.). In some embodiments, the information indicatingan AI/ML capability of the UE may also or instead include informationindicating whether the UE can assist with training.

In some embodiments, the information sent at 1010 may includeinformation indicating an AI/ML capability type of the UE. The AI/MLcapability type may identify whether the UE supports AI/ML optimizationof one or more components of the air interface of the device. Forexample, the AI/ML capability type may be one of a plurality of AI/MLcapability types, where each AI/ML capability type corresponds tosupport for a different level of AI/ML capability. For example, theplurality of AI/ML capability types may include an AI/ML capability typethat indicates the UE supports deep learning. As another example, theplurality of AI/ML capability types may include different types thatindicate different combinations of air interface components that areoptimizable by AI/ML. For example, the plurality of AI/ML capabilitytypes may include one or more of the following types:

-   -   a type corresponding to support for AI/ML based optimization of        all baseband signal processing components, such as coding        (channel coding or joint source and channel coding), modulation        and waveform generation (e.g., similar to the examples shown in        FIGS. 7A and 7B);    -   a type corresponding to support for AI/ML based optimization of        coding and modulation, but not waveform generation (e.g.,        similar to the examples shown in FIGS. 8A and 8B);    -   a type corresponding to support for AI/ML based optimization of        modulation and waveform generation, but not coding (e.g.,        similar to the example shown in FIG. 9);    -   a type corresponding to support for AI/ML based optimization of        modulation, but not coding and waveform generation (e.g.,        similar to the example shown in FIG. 10);    -   a type corresponding to support for AI/ML based optimization of        waveform generation, but not coding and modulation (e.g.,        similar to the example shown in FIG. 11).

In some embodiments, the information sent by the UE to the BS at 1010may be sent by the UE to the BS as part of an initial access procedureto access the network. In other embodiments, the information may also orinstead be sent by the UE in response to a capability enquiry from theBS (not shown).

After receiving AI/ML capability information from the UE indicating thatthe UE supports AI/ML and can assist with training, the BS sends atraining request to the UE at 1012 to trigger a training phase 1050. Insome embodiments, the training request may be sent to the UE through DCI(dynamic signaling) on a downlink control channel or on a data channel.For example, in some embodiments the training request may be sent to theUE as UE specific or UE common DCI. For example, UE common DCI may beused to send a training request to all UEs or a group of UEs.

The UE may send a response to the training request to the BS, asindicated at 1014. This response may confirm that the UE has entered atraining mode. However, such a response can be optional and may not besent by a UE in some embodiments. At 1016 the BS starts the trainingphase 1050 by sending a training signal that includes a trainingsequence or training data to the UE. In some embodiments, the BS maysend a training sequence/training data to the UE after a certainpredefined time gap following transmission of the training request at1012. In other embodiments, the BS may immediately transmit a trainingsequence/training data to the UE after transmitting the training requestat 1012. In still other embodiments, the BS may wait until it hasreceived a response to the training request from the UE beforetransmitting the training sequence/training data to the UE.

Non-limiting examples of channels that may be used by the BS to sendtraining sequences/training data to UE include:

-   -   Dynamic control channel: When the number of bits required to        send the training sequence/training data is less than a certain        threshold, a dynamic control channel may be used to send the        training sequence/training data. In some embodiment, several        levels of bit lengths may be defined. The different bit lengths        may correspond to different DCI formats or different DCI        payloads. The same DCI can be used for carrying training        sequences/data for different AI/ML modules. In some embodiments,        a DCI field may contain information indicating an AI/ML module        the training sequence/training data is to be used to train.    -   Data channel: In some embodiments, a data channel may be used to        carry a training sequence/training data. In such embodiments,        the payload of the data channel depends on the training sequence        length or the amount of training data that is to be sent. The        DCI used to schedule such a data channel can carry the        information required for decoding the data channel and AI/ML        module indicator(s) to indicate which AI/ML module(s) the        training sequence/data is for.    -   RRC channel: In some embodiments, training sequences/training        data can be sent to UE via RRC signaling.

For its part, the UE starts to search for a training signal (e.g., atraining sequence or training data) sent by the network after sendingback a response to the training request at 1014 or after receiving thetraining request at 1012 with or without a predefined time gap. Thechannel resource and the transmission parameters for the trainingsignal, such as MCS and demodulation reference signal (DMRS), can bepredefined or preconfigured (for example by RRC signaling) or signaledby dynamic control signaling (similar to the detection of DCI for ascheduled data channel). In some embodiments, the trainingsequence/training data may be carried in a dynamic control channeldirectly (e.g., certain bits in a dynamic control channel may bereserved for carrying training sequence/training data).

At 1018 the UE sends a training response message to the BS that includesfeedback information based on processing of the received trainingsignal. In some embodiments, the training response message may includefeedback information indicating an updated training sequence for aniterative training process (e.g., for autoencoder based ML) or certaintype(s) of measurement results to help Tx/Rx to further train or refinethe training of a NN, e.g., for enforcement learning. In someembodiments, such measurements may include, for example, the errormargin obtained by the UE in receiving the training sequence/data fromthe BS. For example the measurement results may include informationindicating the mean square of errors and/or an error direction (e.g.,error increase or decrease). In some embodiments, the training responsemessage may also or instead include other feedback information, such asan adjustment step size and direction (e.g., increase or decrease by Xamount, where X is the adjustment step size). In some cases, themeasurement results or feedback may be provided implicitly. For examplethe adjustment of beamforming can be indicated by the beam direction ofthe feedback signal. In some embodiments, the training response messagemay be sent by the UE through an uplink (UL) control channel. In otherembodiments, the training response message may be partially or entirelysent through an UL data channel.

An AI/ML module that includes one or more AI/ML components, such as aneural network, is trained in the network based on the received trainingresponse message from the UE. In FIG. 12, this training is indicated at1019. For example, the parameters of an AI/ML module, such as neuralnetwork weights, may be updated/modified based on measurement resultsreturned by the UE. In some embodiments this training may be performedat least in part in the BS, while in other embodiments the training maybe performed in part or in whole by another network device, such as acentralized AI/ML server (not shown). At 1020, the BS sends informationto the UE to update AI/ML parameters, such as neural network weights, inorder to optimize one or more aspects of the air interface between theUE and BS. In some embodiments this training process is doneiteratively, as indicated at 1040, whereby the BS repeatedly transmitstraining sequence/data and iteratively refines AI/ML parameters based ontraining response messages from the UE. In some embodiments thisiterative process may continue until one or more target criteria issatisfied or until a predefined number of iterations have occurred. Itshould be noted that not all embodiments involve AI/ML functionality atUEs and therefore AI/ML parameters need not necessarily be signaled to aUE in all embodiments. At 1022, the BS terminates the training processby sending a termination signal to the UE indicating the training phaseis finished, in response to which the UE transitions to a normaloperation phase 1060. In some embodiments, the training terminationsignal may be transmitted to the UE through dynamic signaling. In thenormal operations phase 1060 the UE and BS may then communicate via theupdated air interface.

In some embodiments, the information exchange procedure shown in FIG. 12occurs at least partially in the Radio Resource Control (RRC) layer.

In some embodiments, the information exchange procedure shown in FIG. 12occurs at least partially in a Medium Access Control (MAC) layer. Forexample, the information exchange signaling may be carried by a MACcontrol element (MAC CE) implemented as a special bit string in alogical channel ID (LCID) field of a MAC header.

In the example embodiment shown in FIG. 12, the AI/ML training isperformed in the network and the results of the training are sent to theUE, which may be referred to as network oriented training. In otherembodiments, training may take place jointly at the UE and in thenetwork.

FIG. 13 is a signal flow diagram 1100 of an example of an over the airinformation exchange procedure for a training phase of machine learningcomponents enabling device-specific tailoring/customization of an airinterface, in accordance with an embodiment of this disclosure in whichthe training takes place jointly at the UE and BS.

In the signal flow diagram 1100, a UE and a BS or other network deviceare involved in an information exchange for an AI/ML training phase1150. The information exchange procedure begins with the UE sendinginformation indicating an AI/ML capability of the UE to the BS at 1110.The information indicating an AI/ML capability of the UE may include thesame or similar information to that described above with reference tothe example embodiment shown in FIG. 12, but in this example theinformation further also indicates that the UE is capable of joint AI/MLtraining with the network.

In some embodiments, the information sent by the UE to the BS at 1110may be sent as part of an initial access procedure to access thenetwork. In other embodiments, the information may also or instead besent by the UE in response to a capability enquiry from the BS (notshown).

After receiving AI/ML capability information from the UE indicating thatthe UE supports network and UE joint AI/ML training, the BS sends atraining request to the UE at 1112 to trigger a training phase 1150. Insome embodiments, the training request may be sent to the UE through DCI(dynamic signaling) on a downlink control channel or on a data channel.For example, in some embodiments the training request may be sent to theUE with UE specific or UE common DCI. For example, UE common DCI may beused to send a training request to all UEs or a group of UEs. In someembodiments, the training request may be set to the UE via RRCsignaling. In some embodiments, the training request may include initialtraining setting(s)/parameter(s), such as initial NN weights.

In some embodiments, the BS may also send AI/ML related information tothe UE to facilitate joint training such as:

-   -   Information indicating which AI/ML module is to be trained if        there is more than one AI/ML module that is trainable;    -   Information about the AI/ML algorithm and initial        setting/parameters.

The AI/ML related information may be sent as part of the trainingrequest sent at 1112 or may be sent separately from the trainingrequest. The AI/ML related information sent to the UE, such asinformation indicating AI/ML algorithm(s) and setting/parameters, mayhave been selected by the BS or another network device based at least inpart on the AI/ML capability information received from the UE. In someembodiments, the AI/ML related information may include an instructionfor the UE to download initial AI/ML algorithm(s) and/orsetting(s)/parameter(s), in response to which the UE may then downloadinitial AI/ML algorithms and/or setting(s)/parameter(s) in accordancewith the instruction.

In some embodiments, after the UE has received the training request andinitial training information from the network, the UE may send aresponse to the training request to the BS, as indicated at 1114 in FIG.13. This response may confirm that the UE has entered a training mode.However, such a response can be optional and may not be sent by a UE insome embodiments.

At 1116 the BS starts the training phase 1150 by sending a trainingsignal that includes a training sequence or training data to the UE. Insome embodiments, the BS may send a training sequence/training data tothe UE after a certain predefined time gap following transmission of thetraining request at 1112. In other embodiments, the BS may immediatelytransmit a training sequence/training data to the UE after transmittingthe training request at 1112. In still other embodiments, the BS maywait until it has received a response to the training request from theUE before transmitting the training sequence/training data to the UE. Asnoted above, in some embodiments the BS notifies the UE which AI/MLmodule(s)/component(s) is/are to be trained by including information inthe training request that identifies one or more AI/MLmodules/components or by sending such information to the UE in aseparate communication. By doing so, the BS informs the UE which AI/MLmodules(s)/component(s) is/are to be trained based on the trainingsignal transmitted by the BS at 1116. Non-limiting examples of channelsthat may be used by the BS to send training sequences or training datato UE include those discussed above with reference to FIG. 12, namely adynamic control channel, a data channel and/or RRC channel.

Similar to the UE in the example embodiment show in FIG. 12, the UE inthe example embodiment shown in FIG. 13 may start to search for atraining signal (e.g., a training sequence or training data) aftersending back a response to the training request at 1114 or afterreceiving the training request at 1112 with or without a predefined timegap. The channel resource and the transmission parameters for thetraining signal, such as MCS and DMRS, can be predefined orpreconfigured (e.g., by RRC signaling) or signaled by dynamic controlsignaling. In some embodiments, the training sequence/training data maybe carried in a dynamic control channel directly (e.g., certain bits ina dynamic control channel may be reserved for carrying trainingsequence/training data).

At 1118 the UE sends a training response message to the BS. In someembodiments, the training response message may include feedbackinformation indicating an updated training sequence for an iterativetraining process (e.g., for autoencoder based ML) or certain type(s) ofmeasurement results to help further train or refine the training of aNN, e.g., for enforcement learning. In some embodiments, suchmeasurements may include, for example, the error margin obtained by theUE in receiving the training sequence/data from the BS. For example themeasurement results may include information indicating the mean squareof errors and/or an error direction (e.g., error increase or decrease).In some embodiments, the training response message may also or insteadinclude other feedback information, such as an adjustment step size anddirection (e.g., increase or decrease by X amount, where X is theadjustment step size). In some cases, the measurement results orfeedback may be provided implicitly. For example the adjustment ofbeamforming can be indicated by the beam direction of the feedbacksignal. In some embodiments, the training response message may be sentby the UE through an uplink (UL) control channel. In other embodiments,the training response message may be partially or entirely sent throughan UL data channel.

In this embodiment, training of an AI/ML module that includes one ormore AI/ML components takes place jointly in the network and at the UE,as indicated at 1119 in FIG. 13. For example, parameters of an AI/MLmodule, such as neural network weights, may be updated/modified based onmeasurement results returned by the UE for the training sequence/datathat was transmitted by the BS.

In some embodiments, the UE and BS exchange updates of the trainingsetup and parameters, such as neural network weights, in order tooptimize one or more aspects of the air interface between the UE and BS,as indicated at 1120 in FIG. 13. In other embodiments, the UE and/or theBS may be able to update the training setup and parameters autonomouslybased on their own training process at 1119 without the furtherinformation exchange indicated at 1120.

In some embodiments this training process is done iteratively, asindicated at 1140, whereby the BS repeatedly transmits trainingsequence/data and the UE and BS iteratively refine AI/ML parametersbased on training response messages from the UE. In some embodimentsthis iterative process may continue until one or more target criteria issatisfied or until a predefined number of iterations have occurred. Insome embodiments, the training sequence/data may be updated during theiterative training process.

At 1122, the BS terminates the training process by sending a terminationsignal to the UE indicating the training phase is finished, in responseto which the UE transitions to a normal operation phase 1160. In someembodiments, the UE may initiate termination of the training phase bysending a termination recommendation signal to the BS. In the normaloperations phase 1160 the UE and BS may then communicate via the updatedair interface.

In some embodiments, the AI/ML algorithms and/or parameters may havebeen pre-downloaded by the UE. In some embodiments, the AI/ML capabilityinformation the UE sends at 1110 may include information indicatingpre-downloaded AI/ML algorithms and parameters. In such embodiments, theBS may transmit a download instruction to a UE to instruct the UE todownload a selected AI/ML algorithm or parameters if the AI/MLcapability information received from the UE indicates the selected AI/MLalgorithm or parameters have not been pre-downloaded by the UE.

In some embodiments, the information exchange procedure shown in FIG. 13occurs at least partially in the RRC layer.

In some embodiments, the information exchange procedure shown in FIG. 13occurs at least partially in a MAC layer. For example, the informationexchange signaling may be carried by a MAC CE implemented as a specialbit string in a LCID field of a MAC header.

It should be understood that the specific AI/ML component architecturesthat may be used in embodiments of the present disclosure may bedesigned based on the particular application. For example, where anAI/ML component is implemented with a deep neural network (DNN), thespecific DNN architecture that should be used for a given application(e.g., joint coding and modulation optimization or individual waveformgeneration optimization) may be standardized (e.g., in agreed uponindustry standards). For example, standardization may include a standarddefinition of the type(s) of neural network to be used, and certainparameters of the neural network (e.g., number of layers, number ofneurons in each layer, etc.). Standardization may beapplication-specific. For example, a table may be used to list thestandard-defined neural network types and parameters to be used forspecific applications. In the context of the wireless system 100 of FIG.1, standardized definitions may be stored in the memory of the BS 170,to enable the BS 170 to select the appropriate DNN architecture andparameters to be trained for a particular wireless communicationscenario.

As discussed above with respect to FIGS. 12 and 13, training of DNN(s)(e.g., a single DNN implementing coding, modulation and/or waveformgeneration, or separate DNNs for each) or other AI/ML components may beperformed at a BS or jointly at a BS and a UE, and may be performed atthe time of initial setup and association between the BS and UE. In someexamples, it may be sufficient for the BS and/or the UE to train anAI/ML component, e.g., DNN(s), at the time of setup. As well, trainingor re-training may also be performed on-the-fly, for example in responseto significant change in the UE or BS and/or the environment (e.g.,addition of new UE(s), disassociation of a UE, significant change in UEmobility, change in UE state or significant change in channel, amongother possibilities).

In some examples, training of the AI/ML components, such as DNNs, at theBS and/or UE may be performed offline, for example using data collectedby the BS or UE. The collected data may represent different wirelesscommunication scenarios, such as different times of day, different daysof the week, different traffic levels, etc. Training may be performedfor a particular scenario, to generate different sets of DNN weights fordifferent scenarios. The different sets of weights may be stored inassociation with the different specific scenarios (e.g., in a look-uptable), for example in the memory of the BS or UE. The BS or UE may thenselect and use a particular set of weights for the DNN(s), in accordancewith the specific scenario. For example, the BS or UE may determine thatit is handling communications for a weekend evening (e.g., usinginformation from an internal clock and/or calendar) and use thecorresponding set of weights to implement the DNN(s) for coding,modulation and/or waveform generation. This would result in thetransmitter of the BS 170 performing coding, modulation and/or waveformgeneration suitable for a weekend evening.

In some embodiments, offline and on-the-fly training may be appliedjointly. For example, on-the-fly re-trainining may be performed toupdate training that was previously performed offline. For example, a BSand/or UE may also retrain AI/ML components such as DNN(s) on-the-fly,in response to dynamic changes in the environment and/or in the UE orBS, as discussed above. Thus, the BS or UE may update the table ofweights dynamically. In some examples, the table of weights may includesets of weights that are standardized (e.g., defined in standards forvery common scenarios) and may also include sets of weights that aregenerated offline and/or on-the-fly for certain scenarios.

The BS may provide an indexed table of weights and associated scenariosto the UE. The BS may instruct the UE a selected set of weights to use,for example by indicating the corresponding index of a selected set ofweights. The BS and/or UE may retrain their AI/ML components and updatetheir tables of weights (e.g., in response to a new scenario) andcommunicate the updated tables to one another, e.g., on a periodic oraperiodic basis.

FIG. 14 is a signal flow diagram 1200 of an example of an over the airinformation exchange procedure for a normal operations phase 1260 ofmachine learning components enabling on-the-fly device-specifictailoring/customization of an air interface, in accordance with anembodiment of this disclosure. In this embodiment, the on-the-fly updateof AI/ML parameters may be triggered by the network during the normaloperation phase 1260, as indicated at 1210. The network may trigger theon-the-fly update by sending updated AI/ML parameters, such as DNNweights. In this embodiment the on-the-fly update may also or instead betriggered by the UE during the normal operation phase 1260, as indicatedat 1212. The UE may trigger the on-the-fly update by sending updatedAI/ML parameters, such as DNN weights to the BS if the UE is capable ofself-training. Otherwise, the trigger that the UE sends the BS at 1212may simply comprise a request for an update from the BS. In addition orinstead of being triggered by the BS and/or the UE, an on-the-fly updateduring the normal operation phase 1260 may occur on a periodic oraperiodic basis, and may involve a mutual information update exchange,as indicated at 1214.

FIG. 15 is a signal flow diagram 1300 of an example of an over the airinformation exchange procedure for a re-training phase of machinelearning components enabling device-specific tailoring/customization ofan air interface, in accordance with an embodiment of this disclosure.

In the signal flow diagram 1300, a UE and a BS or other network deviceare involved in an information exchange for an AI/ML re-training phase1350. In this embodiment, the re-training phase may be triggered by thenetwork, as indicated at 1310. In some embodiments, the BS may triggerthe re-training by sending a training request to the UE, e.g., throughDCI, RRC or MAC signaling as discussed earlier with reference to FIGS.12 and 13. In this embodiment the re-training phase may also or insteadbe triggered by the UE, as indicated at 1312. In either case, during there-training phase 1350 the UE and BS exchange re-training signaling asindicated at 1314 in order to facilitate re-training of AI/ML componentsin the network and/or at the UE. For example, in some embodiments there-training signaling may include information exchanges and signalingsuch as that indicated at 1016, 1018 and 1020 in FIG. 12 or at 1116,1118 and 1120 in FIG. 13. In some embodiments, re-training of an AI/MLmodule that includes one or more AI/ML components may take place in thenetwork or jointly in the network and at the UE, as indicated at 1319 inFIG. 15.

In some embodiments this re-training process is done iteratively, asindicated at 1340, whereby the BS repeatedly transmits trainingsequence/data and the UE and BS iteratively refine AI/ML parametersbased on re-training response messages from the UE. In some embodimentsthis iterative process may continue until one or more target criteria issatisfied or until a predefined number of iterations have occurred. Insome embodiments, the re-training sequence/data may be updated duringthe iterative re-training process.

At 1316, the BS terminates the re-training process by sending atermination signal to the UE indicating the re-training phase isfinished, in response to which the UE transitions to a normal operationphase 1360. In some embodiments, the UE may instead initiate terminationof the re-training phase by sending a termination recommendation signalto the BS, as indicated at 1318. In the normal operations phase 1360 theUE and BS may then communicate via the updated air interface resultingfrom the re-training.

The above discussion refers to examples where the network side trainingis performed by the BS. In other examples, AI/ML component training maynot be performed by the BS. For example, referring again to FIG. 1,training may be performed by the core network 130 or elsewhere in thewireless system 100 (e.g., using cloud computing). A BS 170 may simplycollect the relevant data and forward the data to the appropriatenetwork entity (e.g., the core network 130) to perform the necessarytraining. The trained AI/ML component parameters, e.g., weights oftrained DNN(s), may then be provided to the BS 170 and ED(s) 110.

Although the above discussion is in the context of the BS 170 in therole of a transmitter and the ED 110 in the role of a receiver, itshould be understood that the transmitter and receiver roles may bereversed (e.g., for uplink communications). Further, it should beunderstood that the transmitter and receiver roles may be at two or moreEDs 110 a, 110 b, 110 c (e.g., for sidelink communications). The BS 170(or core network 130 or other network entity) may perform the DNNtraining and may provide the trained weights to the ED 110 in order forthe ED 110 to implement the DNN(s) for communicating with the BS 170.

EXAMPLE EMBODIMENTS

The following provides a non-limiting list of additional ExampleEmbodiments of the present disclosure:

Example Embodiment 1. A method in a wireless communication network, themethod comprising:

transmitting, by a first device, information regarding an artificialintelligence or machine learning (AI/ML) capability of the first deviceto a second device over a single air interface between the first deviceand the second device, the information regarding an AI/ML capability ofthe first device identifying whether the first device supports AI/ML foroptimization of at least one air interface configuration over the singleair interface.

Example Embodiment 2. The method of Example Embodiment 1, wherein theinformation regarding an AI/ML capability of the first device comprisesinformation indicating the first device is capable of supporting a typeand/or level of complexity of AI/ML.Example Embodiment 3. The method of Example Embodiment 1 or 2, whereinthe information regarding an AI/ML capability of the first devicecomprises information indicating whether the first device assists withan AI/ML training process for optimization of the at least one airinterface configuration.Example Embodiment 4. The method of any of Example Embodiments 1 to 3,wherein the information regarding an AI/ML capability of the firstdevice comprises information indicating at least one component of the atleast one air interface configuration for which the first devicesupports AI/ML optimization.Example Embodiment 5. The method of Example Embodiment 4, wherein the atleast one component of the at least one air interface configurationincludes at least one of a coding component, a modulation component anda waveform component.Example Embodiment 6. The method of Example Embodiment 4 or 5, whereinthe information indicating at least one component of the at least oneair interface configuration for which the first device supports AI/MLoptimization further comprises information indicating whether the firstdevice supports joint optimization of two or more components of the atleast one air interface configuration.Example Embodiment 7. The method of any of Example Embodiments 1 to 6,wherein transmitting the information regarding an AI/ML capability ofthe first device comprises at least one of:

transmitting the information in response to receiving an enquiry; and

transmitting the information as part of an initial network accessprocedure.

Example Embodiment 8. The method of any of Example Embodiments 1 to 7,further comprising:

receiving an AI/ML training request from the second device; and

after receiving the AI/ML training request, transitioning to an AI/MLtraining mode.

Example Embodiment 9. The method of Example Embodiment 8, whereinreceiving the AI/ML training request comprises receiving the AI/MLtraining request through downlink control information (DCI) on adownlink control channel or RRC signaling or the combination of the DCIand RRC signaling.Example Embodiment 10. The method of Example Embodiment 8 or 9, furthercomprising, transmitting a training request response to the seconddevice to confirm that the first device has transitioned to the AI/MLtraining mode.Example Embodiment 11. The method of any of Example Embodiments 1 to 10,further comprising receiving a training signal from the second devicethat includes a training sequence or training data for training at leastone AI/ML module responsible for one or more components of the at leastone air interface configuration.Example Embodiment 12. The method of Example Embodiment 11, whereinreceiving the training signal comprises receiving the training signal ona dynamic control channel.Example Embodiment 13. The method of Example Embodiment 12, wherein thedynamic control channel includes a dynamic control information (DCI)field containing information indicating an AI/ML module that is to betrained.Example Embodiment 14. The method of Example Embodiment 11, whereinreceiving the training signal comprises receiving the training signal ona scheduled data channel, the method further comprising receivingscheduling information for the data channel on a dynamic control channelthat includes a DCI field containing information indicating an AI/MLmodule that is to be trained.Example Embodiment 15. The method of any of Example Embodiments 11 to14, further comprising, after receiving the training signal,transmitting a training response message to the second device, thetraining response message including feedback information based onprocessing of the received training signal at the first device.Example Embodiment 16. The method of Example Embodiment 15, wherein thefeedback information included in the training response message includesan updated training sequence for an iterative training process.Example Embodiment 17. The method of Example Embodiment 15 or 16,wherein the feedback information included in the training responsemessage includes measurement results based on the received trainingsignal.Example Embodiment 18. The method of Example Embodiment 17, wherein themeasurement results include an error margin obtained by the first devicein receiving the training signal from the second device.Example Embodiment 19. The method of any of Example Embodiments 15 to18, further comprising, after transmitting the training responsemessage, receiving AI/ML update information from the second device, theAI/ML update information including information indicating updated AI/MLparameters for an AI/ML module based on the feedback informationprovided by the first device.Example Embodiment 20. The method of Example Embodiment 19, furthercomprising, updating the AI/ML module in accordance with the updatedAI/ML parameters in order to update the at least one air interfaceconfiguration for receiving transmissions from the second device.Example Embodiment 21. The method of any of Example Embodiments 15 to18, further comprising:

training one or more AI/ML modules at the first device based on thetraining signal received from the second device; and

transmitting AI/ML update information to the second device, the AI/MLupdate information including information indicating updated AI/MLparameters for at least one of the one or more AI/ML modules based onthe training performed by the first device.

Example Embodiment 22. The method of Example Embodiment 21, furthercomprising receiving AI/ML update information from the second device,the AI/ML update information from the second device includinginformation indicating updated AI/ML parameters for at least one of theone or more AI/ML modules based on training of one or more AI/ML modulesat the second device based on feedback information provided in thetraining response message.Example Embodiment 23. The method of Example Embodiment 22, furthercomprising, updating the at least one air interface configuration forreceiving transmissions from the second device by updating the one ormore AI/ML modules in accordance with the updated AI/ML parameters basedon the training performed by the first device and the updated AI/MLparameters received from the second device.Example Embodiment 24. The method of any of Example Embodiments 1 to 23,further comprising:

receiving a training termination signal from the second device; and

after receiving the training termination signal, transitioning the firstdevice from the training mode to a normal operations mode.

Example Embodiment 25. The method of any of Example Embodiments 1 to 24,wherein the first device is user equipment and the second device is anetwork device.Example Embodiment 26. A method in a wireless communication network, themethod comprising:

receiving, by a second device, information regarding an artificialintelligence or machine learning (AI/ML) capability of a first deviceover a single air interface between the first device and the seconddevice, the information regarding an AI/ML capability of the firstdevice identifying whether the first device supports AI/ML foroptimization of at least one air interface configuration over the singleair interface; and

transmitting an AI/ML training request to the first device based atleast in part on the information regarding the AI/ML capability of thefirst device.

Example Embodiment 27. The method of Example Embodiment 26, wherein theinformation regarding an AI/ML capability of the first device comprisesinformation indicating the first device is capable of supporting a typeand/or level of complexity of AI/ML.Example Embodiment 28. The method of Example Embodiment 26 or 27,wherein the information regarding an AI/ML capability of the firstdevice comprises information indicating whether the first device assistswith an AI/ML training process for optimization of the at least on airinterface configuration.Example Embodiment 29. The method of any of Example Embodiments 26 to28, wherein the information regarding an AI/ML capability of the firstdevice comprises information indicating at least one component of the atleast one air interface configuration for which the first devicesupports AI/ML optimization.Example Embodiment 30. The method of Example Embodiment 29, wherein theat least one component of the at least one air interface configurationincludes at least one of a coding component, a modulation component anda waveform component.Example Embodiment 31. The method of Example Embodiment 29 or 30,wherein the information indicating at least one component of the atleast one air interface configuration for which the first devicesupports AI/ML optimization further comprises information indicatingwhether the first device supports joint optimization of two or morecomponents of the at least one air interface configuration.Example Embodiment 32. The method of any of Example Embodiments 26 to31, wherein receiving the information regarding an AI/ML capability ofthe first device comprises receiving the information as part of aninitial network access procedure for the first device.Example Embodiment 33. The method of any of Example Embodiments 26 to32, wherein transmitting the AI/ML training request comprisestransmitting the AI/ML training request through downlink controlinformation (DCI) on a downlink control channel or RRC signaling or thecombination of the DCI and RRC signaling.Example Embodiment 34. The method of Example Embodiment 33, furthercomprising, receiving a training request response from the deviceconfirming that the device has transitioned to an AI/ML training mode.Example Embodiment 35. The method of any of Example Embodiments 26 to34, further comprising transmitting a training signal to the firstdevice, the training signal including a training sequence or trainingdata for training at least one AI/ML module responsible for one or morecomponents of the at least one air interface configuration.Example Embodiment 36. The method of Example Embodiment 35, whereintransmitting the training signal comprises transmitting the trainingsignal on a dynamic control channel.Example Embodiment 37. The method of Example Embodiment 36, wherein thedynamic control channel includes a dynamic control information (DCI)field containing information indicating an AI/ML module that is to betrained.Example Embodiment 38. The method of Example Embodiment 35, whereintransmitting the training signal comprises transmitting the trainingsignal on a scheduled data channel.Example Embodiment 39. The method of Example Embodiment 38, furthercomprising transmitting scheduling information for the data channel on adynamic control channel that includes a DCI field containing informationindicating an AI/ML module that is to be trained.Example Embodiment 40. The method of any of Example Embodiments 35 to39, further comprising receiving a training response message from thefirst device, the training response message including feedbackinformation based on processing of the received training signal at thefirst device.Example Embodiment 41. The method of Example Embodiment 40, wherein thefeedback information included in the training response message includesan updated training sequence for an iterative training process.Example Embodiment 42. The method of Example Embodiment 40 or 41,wherein the feedback information included in the training responsemessage includes measurement results based on the received trainingsignal.Example Embodiment 43. The method of Example Embodiment 42, wherein themeasurement results include an error margin obtained by the first devicein receiving the training signal.Example Embodiment 44. The method of any of Example Embodiments 40 to43, further comprising:

training one or more AI/ML modules based on the feedback informationprovided in the training response message from the first device.

Example Embodiment 45. The method of Example Embodiment 44, furthercomprising:

transmitting AI/ML update information to the first device, the AI/MLupdate information including information indicating updated AI/MLparameters for at least one of the one or more AI/ML modules based onthe training.

Example Embodiment 46. The method of any of Example Embodiments 40 to45, further comprising:

receiving AI/ML update information from the first device, the AI/MLupdate information from the first device including informationindicating updated AI/ML parameters for at least one of the one or moreAI/ML modules based on training of one or more AI/ML modules at thefirst device based on the training signal.

Example Embodiment 47. The method of Example Embodiment 46, furthercomprising updating the at least one air interface configuration fortransmitting to the first device by updating the one or more AI/MLmodules in accordance with the updated AI/ML parameters transmitted tothe first device and the updated AI/ML parameters received from thefirst device.Example Embodiment 48. The method of any of Example Embodiments 26 to47, further comprising:

transmitting a training termination signal to the first device toindicate that a training phase has finished.

Example Embodiment 49. The method of any of Example Embodiments 26 to48, wherein the first device is user equipment and the second device isa network device.Example Embodiment 50. An apparatus comprising:

a wireless interface;

a processor operatively coupled to the wireless interface; and

a computer readable storage medium operatively coupled to the processor,the computer readable storage medium storing programming for executionby the processor, the programming comprising instructions to:

-   -   transmit, from a first device via the wireless interface,        information regarding an artificial intelligence or machine        learning (AI/ML) capability of the first device to a second        device over a single air interface between the first device and        the second device, the information regarding an AI/ML capability        of the first device identifying whether the first device        supports AI/ML for optimization of at least one air interface        configuration over the single air interface.        Example Embodiment 51. The apparatus of Example Embodiment 50,        wherein the information regarding an AI/ML capability of the        first device comprises information indicating the first device        is capable of supporting a type and/or level of complexity of        AI/ML.        Example Embodiment 52. The apparatus of Example Embodiment 50 or        51, wherein the information regarding an AI/ML capability of the        first device comprises information indicating whether the first        device assists with an AI/ML training process for optimization        of the at least one air interface configuration.        Example Embodiment 53. The apparatus of any of Example        Embodiments 50 to 52, wherein the information regarding an AI/ML        capability of the first device comprises information indicating        at least one component of the at least one air interface        configuration for which the first device supports AI/ML        optimization.        Example Embodiment 54. The apparatus of Example Embodiment 53,        wherein the at least one component of the at least one air        interface configuration includes at least one of a coding        component, a modulation component and a waveform component.        Example Embodiment 55. The apparatus of Example Embodiment 53 or        54, wherein the information indicating at least one component of        the at least one air interface configuration for which the first        device supports AI/ML optimization further comprises information        indicating whether the first device supports joint optimization        of two or more components of the at least one air interface        configuration.        Example Embodiment 56. The apparatus of any of Example        Embodiments 50 to 55, wherein the instructions to transmit the        information regarding an AI/ML capability of the first device        comprises at least one of:

instructions to transmit the information in response to receiving anenquiry; and

instructions to transmit the information as part of an initial networkaccess procedure.

Example Embodiment 57. The apparatus of any of Example Embodiments 50 to56, wherein the programming further comprises instructions to:

receive an AI/ML training request from the second device; and

after receiving the AI/ML training request, transition to an AI/MLtraining mode.

Example Embodiment 58. The apparatus of Example Embodiment 57, whereinthe instructions to receive the AI/ML training request comprisesinstructions to receive the AI/ML training request through downlinkcontrol information (DCI) on a downlink control channel or RRC signalingor the combination of the DCI and RRC signaling.Example Embodiment 59. The apparatus of Example Embodiment 57 or 58,wherein the programming further comprises instructions to transmit atraining request response to the second device to confirm that the firstdevice has transitioned to the AI/ML training mode.Example Embodiment 60. The apparatus of any of Example Embodiments 50 to59, wherein the programming further comprises instructions to receive atraining signal from the second device that includes a training sequenceor training data for training at least one AI/ML module responsible forone or more components of the at least one air interface configuration.Example Embodiment 61. The apparatus of Example Embodiment 60, whereinthe instructions to receive the training signal comprise instructions toreceive the training signal on a dynamic control channel.Example Embodiment 62. The apparatus of Example Embodiment 61, whereinthe dynamic control channel includes a dynamic control information (DCI)field containing information indicating an AI/ML module that is to betrained.Example Embodiment 63. The apparatus of Example Embodiment 60, whereinthe instructions to receive the training signal comprise instructions toreceive the training signal on a scheduled data channel, the programfurther comprising instructions to receive scheduling information forthe data channel on a dynamic control channel that includes a DCI fieldcontaining information indicating an AI/ML module that is to be trained.Example Embodiment 64. The apparatus of any of Example Embodiments 60 to63, wherein the programming further comprises instructions to:

transmit a training response message to the second device afterreceiving the training signal, the training response message includingfeedback information based on processing of the received training signalat the first device.

Example Embodiment 65. The apparatus of Example Embodiment 64, whereinthe feedback information included in the training response messageincludes an updated training sequence for an iterative training process.Example Embodiment 66. The apparatus of Example Embodiment 64 or 65,wherein the feedback information included in the training responsemessage includes measurement results based on the received trainingsignal.Example Embodiment 67. The apparatus of Example Embodiment 66, whereinthe measurement results include an error margin obtained by the firstdevice in receiving the training signal from the second device.Example Embodiment 68. The apparatus of any of Example Embodiments 64 to67, wherein the programming further comprises instructions to:

receive AI/ML update information from the second device aftertransmitting the training response message, the AI/ML update informationincluding information indicating updated AI/ML parameters for an AI/MLmodule based on the feedback information provided by the first device.

Example Embodiment 69. The apparatus of Example Embodiment 68, whereinthe programming further comprises instructions to update the AI/MLmodule in accordance with the updated AI/ML parameters in order toupdate the at least one air interface configuration for receivingtransmissions from the second device.Example Embodiment 70. The apparatus of any of Example Embodiments 64 to67, wherein the programming further comprises instructions to:

train one or more AI/ML modules at the first device based on thetraining signal received from the second device; and

transmit AI/ML update information to the second device, the AI/ML updateinformation including information indicating updated AI/ML parametersfor at least one of the one or more AI/ML modules based on the trainingperformed by the first device.

Example Embodiment 71. The apparatus of Example Embodiment 70, whereinthe programming further comprises instructions to receive AI/ML updateinformation from the second device, the AI/ML update information fromthe second device including information indicating updated AI/MLparameters for at least one of the one or more AI/ML modules based ontraining of one or more AI/ML modules at the second device based onfeedback information provided in the training response message.Example Embodiment 72. The apparatus of Example Embodiment 71, whereinthe programming further comprises instructions to update the at leastone air interface configuration for receiving transmissions from thesecond device by updating the one or more AI/ML modules in accordancewith the updated AI/ML parameters based on the training performed by thefirst device and the updated AI/ML parameters received from the seconddevice.Example Embodiment 73. The apparatus of any of Example Embodiments 50 to72, wherein the programming further comprises instructions to:

receive a training termination signal from the second device; and

after receiving the training termination signal, transition the firstdevice from the training mode to a normal operations mode.

Example Embodiment 74. The apparatus of any of Example Embodiments 50 to73, wherein the first device is user equipment and the second device isa network device.Example Embodiment 75. An apparatus comprising:

a wireless interface;

a processor operatively coupled to the wireless interface; and

a computer readable storage medium operatively coupled to the processor,the computer readable storage medium storing programming for executionby the processor, the programming comprising instructions to:

-   -   receive, by a second device via the wireless interface,        information regarding an artificial intelligence or machine        learning (AI/ML) capability of a first device over a single air        interface between the first device and the second device, the        information regarding an AI/ML capability of the first device        identifying whether the first device supports AI/ML for        optimization of at least one air interface configuration over        the single air interface; and    -   transmit an AI/ML training request to the first device based at        least in part on the information regarding the AI/ML capability        of the first device.        Example Embodiment 76. The apparatus of Example Embodiment 75,        wherein the information regarding an AI/ML capability of the        first device comprises information indicating the first device        is capable of supporting a type and/or level of complexity of        AI/ML.        Example Embodiment 77. The apparatus of Example Embodiment 75 or        76, wherein the information regarding an AI/ML capability of the        first device comprises information indicating whether the first        device assists with an AI/ML training process for optimization        of the at least on air interface configuration.        Example Embodiment 78. The apparatus of any of Example        Embodiments 75 to 77, wherein the information regarding an AI/ML        capability of the first device comprises information indicating        at least one component of the at least one air interface        configuration for which the first device supports AI/ML        optimization.        Example Embodiment 79. The apparatus of Example Embodiment 78,        wherein the at least one component of the at least one air        interface configuration includes at least one of a coding        component, a modulation component and a waveform component.        Example Embodiment 80. The apparatus of Example Embodiment 78 or        79, wherein the information indicating at least one component of        the at least one air interface configuration for which the first        device supports AI/ML optimization further comprises information        indicating whether the first device supports joint optimization        of two or more components of the at least one air interface        configuration.        Example Embodiment 81. The apparatus of any of Example        Embodiments 75 to 80, wherein receiving the information        regarding an AI/ML capability of the first device comprises        receiving the information as part of an initial network access        procedure for the first device.        Example Embodiment 82. The apparatus of any of Example        Embodiments 75 to 81, wherein transmitting the AI/ML training        request comprises transmitting the AI/ML training request        through downlink control information (DCI) on a downlink control        channel or RRC signaling or the combination of the DCI and RRC        signaling.        Example Embodiment 83. The apparatus of Example Embodiment 82,        wherein the programming further comprises instructions to        receive a training request response from the device confirming        that the device has transitioned to an AI/ML training mode.        Example Embodiment 84. The apparatus of any of Example        Embodiments 75 to 83, wherein the programming further comprises        instructions to transmit a training signal to the first device,        the training signal including a training sequence or training        data for training at least one AI/ML module responsible for one        or more components of the at least one air interface        configuration.        Example Embodiment 85. The apparatus of Example Embodiment 84,        wherein transmitting the training signal comprises transmitting        the training signal on a dynamic control channel.        Example Embodiment 86. The apparatus of Example Embodiment 85,        wherein the dynamic control channel includes a dynamic control        information (DCI) field containing information indicating an        AI/ML module that is to be trained.        Example Embodiment 87. The apparatus of Example Embodiment 84,        wherein transmitting the training signal comprises transmitting        the training signal on a scheduled data channel.        Example Embodiment 88. The apparatus of Example Embodiment 87,        wherein the programming further comprises instructions to        transmit scheduling information for the data channel on a        dynamic control channel that includes a DCI field containing        information indicating an AI/ML module that is to be trained.        Example Embodiment 89. The apparatus of any of Example        Embodiments 84 to 88, wherein the programming further comprises        instructions to receive a training response message from the        first device, the training response message including feedback        information based on processing of the received training signal        at the first device.        Example Embodiment 90. The apparatus of Example Embodiment 89,        wherein the feedback information included in the training        response message includes an updated training sequence for an        iterative training process.        Example Embodiment 91. The apparatus of Example Embodiment 89 or        90, wherein the feedback information included in the training        response message includes measurement results based on the        received training signal.        Example Embodiment 92. The apparatus of Example Embodiment 91,        wherein the measurement results include an error margin obtained        by the first device in receiving the training signal.        Example Embodiment 93. The apparatus of any of Example        Embodiments 89 to 92, wherein the programming further comprises        instructions to:

train one or more AI/ML modules based on the feedback informationprovided in the training response message from the first device.

Example Embodiment 94. The apparatus of Example Embodiment 93, whereinthe programming further comprises instructions to:

transmit AI/ML update information to the first device, the AI/ML updateinformation including information indicating updated AI/ML parametersfor at least one of the one or more AI/ML modules based on the training.

Example Embodiment 95. The apparatus of any of Example Embodiments 89 to94, wherein the programming further comprises instructions to:

receive AI/ML update information from the first device, the AI/ML updateinformation from the first device including information indicatingupdated AI/ML parameters for at least one of the one or more AI/MLmodules based on training of one or more AI/ML modules at the firstdevice based on the training signal.

Example Embodiment 96. The apparatus of Example Embodiment 95, whereinthe programming further comprises instructions to update the at leastone air interface configuration for transmitting to the first device byupdating the one or more AI/ML modules in accordance with the updatedAI/ML parameters transmitted to the first device and the updated AI/MLparameters received from the first device.Example Embodiment 97. The apparatus of any of Example Embodiments 75 to96, wherein the programming further comprises instructions to:

transmit a training termination signal to the first device to indicatethat a training phase has finished.

Example Embodiment 98. The apparatus of any of Example Embodiments 75 to97, wherein the first device is user equipment and the second device isa network device.Example Embodiment 99. An apparatus comprising:

a transmitting module configured to transmit, from a first device,information regarding an artificial intelligence or machine learning(AI/ML) capability of the first device to a second device over an airinterface between the first device and the second device, theinformation regarding an AI/ML capability of the first deviceidentifying whether the first device supports AI/ML for optimization ofat least one air interface component over the air interface.

Example Embodiment 100. The apparatus of Example Embodiment 99, whereinthe information regarding an AI/ML capability of the first devicecomprises information indicating the first device is capable ofsupporting a type and/or level of complexity of AI/ML.Example Embodiment 101. The apparatus of Example Embodiment 99 or 100,wherein the information regarding an AI/ML capability of the firstdevice comprises information indicating whether the first device assistswith an AI/ML training process for optimization of the at least one airinterface component.Example Embodiment 102. The apparatus of any of Example Embodiments 99to 101, wherein the information regarding an AI/ML capability of thefirst device comprises information indicating at least one component ofthe at least one air interface component for which the first devicesupports AI/ML optimization.Example Embodiment 103. The apparatus of Example Embodiment 102, whereinthe at least one air interface component includes at least one of acoding component, a modulation component and a waveform component.Example Embodiment 104. The apparatus of Example Embodiment 102 or 103,wherein the information indicating at least one component of the atleast one air interface component for which the first device supportsAI/ML optimization further comprises information indicating whether thefirst device supports joint optimization of two or more components ofthe at least one air interface component.Example Embodiment 105. The apparatus of any of Example Embodiments 99to 104, wherein the transmitting module is configured to transmit theinformation regarding an AI/ML capability of the first device inresponse to receiving an enquiry or as part of an initial network accessprocedure.Example Embodiment 106. The apparatus of any of Example Embodiments 99to 105, further comprising:

a receiving module configured to receive an AI/ML training request fromthe second device; and

a processing module configured to transition to an AI/ML training modeafter the AI/ML training request is received.

Example Embodiment 107. The apparatus of Example Embodiment 106, whereinthe receiving module is configured to receive the AI/ML training requestthrough downlink control information (DCI) on a downlink control channelor RRC signaling or the combination of the DCI and RRC signaling.Example Embodiment 108. The apparatus of Example Embodiment 106 or 107,wherein the transmitting module is configured to transmit a trainingrequest response to the second device to confirm that the first devicehas transitioned to the AI/ML training mode.Example Embodiment 109. The apparatus of any of Example Embodiments 99to 108, wherein the receiving module is configured to receive a trainingsignal from the second device that includes a training sequence ortraining data for training at least one AI/ML module responsible for oneor more components of the at least one air interface component.Example Embodiment 110. The apparatus of Example Embodiment 109, whereinthe receiving module is configured to receive the training signal on adynamic control channel.Example Embodiment 111. The apparatus of Example Embodiment 110, whereinthe dynamic control channel includes a dynamic control information (DCI)field containing information indicating an AI/ML module that is to betrained.Example Embodiment 112. The apparatus of Example Embodiment 109, whereinthe receiving module is configured to:

receive the training signal on a scheduled data channel; and

receive scheduling information for the data channel on a dynamic controlchannel that includes a DCI field containing information indicating anAI/ML module that is to be trained.

Example Embodiment 113. The apparatus of any of Example Embodiments 109to 112, wherein the transmitting module is configured to:

transmit a training response message to the second device afterreceiving the training signal, the training response message includingfeedback information based on processing of the received training signalat the first device.

Example Embodiment 114. The apparatus of Example Embodiment 113, whereinthe feedback information included in the training response messageincludes an updated training sequence for an iterative training process.Example Embodiment 115. The apparatus of Example Embodiment 113 or 114,wherein the feedback information included in the training responsemessage includes measurement results based on the received trainingsignal.Example Embodiment 116. The apparatus of Example Embodiment 115, whereinthe measurement results include an error margin obtained by the firstdevice in receiving the training signal from the second device.Example Embodiment 117. The apparatus of any of Example Embodiments 113to 116, wherein the receiving module is configured to:

receive AI/ML update information from the second device aftertransmitting the training response message, the AI/ML update informationincluding information indicating updated AI/ML parameters for an AI/MLmodule based on the feedback information provided by the first device.

Example Embodiment 118. The apparatus of Example Embodiment 117, furthercomprising a processing module configured to update the AI/ML module inaccordance with the updated AI/ML parameters in order to update the atleast one air interface component for receiving transmissions from thesecond device.Example Embodiment 119. The apparatus of any of Example Embodiments 113to 116, further comprising a processing module configured to train oneor more AI/ML modules at the first device based on the training signalreceived from the second device, wherein the transmitting module isconfigured to transmit AI/ML update information to the second device,the AI/ML update information including information indicating updatedAI/ML parameters for at least one of the one or more AI/ML modules basedon the training performed by the first device.Example Embodiment 120. The apparatus of Example Embodiment 119, whereinthe receiving module is configured to receive AI/ML update informationfrom the second device, the AI/ML update information from the seconddevice including information indicating updated AI/ML parameters for atleast one of the one or more AI/ML modules based on training of one ormore AI/ML modules at the second device based on feedback informationprovided in the training response message.Example Embodiment 121. The apparatus of Example Embodiment 120, whereinthe processing module is configured to update the at least one airinterface component for receiving transmissions from the second deviceby updating the one or more AI/ML modules in accordance with the updatedAI/ML parameters based on the training performed by the first device andthe updated AI/ML parameters received from the second device.Example Embodiment 122. The apparatus of any of Example Embodiments 99to 121, wherein the receiving module is configured to receive a trainingtermination signal from the second device, and the processing module isconfigured to transition the first device from the training mode to anormal operations mode after the training termination signal isreceived.Example Embodiment 123. The apparatus of any of Example Embodiments 99to 122, wherein the first device is user equipment and the second deviceis a network device.Example Embodiment 124. An apparatus comprising:

a receiving module configured to receive, by a second device,information regarding an artificial intelligence or machine learning(AI/ML) capability of a first device over an air interface between thefirst device and the second device, the information regarding an AI/MLcapability of the first device identifying whether the first devicesupports AI/ML for optimization of at least one air interface componentover the air interface; and

a transmitting module configured to transmit an AI/ML training requestto the first device based at least in part on the information regardingthe AI/ML capability of the first device.

Example Embodiment 125. The apparatus of Example Embodiment 124, whereinthe information regarding an AI/ML capability of the first devicecomprises information indicating the first device is capable ofsupporting a type and/or level of complexity of AI/ML.Example Embodiment 126. The apparatus of Example Embodiment 124 or 125,wherein the information regarding an AI/ML capability of the firstdevice comprises information indicating whether the first device assistswith an AI/ML training process for optimization of the at least on airinterface component.Example Embodiment 127. The apparatus of any of Example Embodiments 124to 126, wherein the information regarding an AI/ML capability of thefirst device comprises information indicating at least one component ofthe at least one air interface component for which the first devicesupports AI/ML optimization.Example Embodiment 128. The apparatus of Example Embodiment 127, whereinthe at least one component of the at least one air interface componentincludes at least one of a coding component, a modulation component anda waveform component.Example Embodiment 129. The apparatus of Example Embodiment 127 or 128,wherein the information indicating at least one component of the atleast one air interface component for which the first device supportsAI/ML optimization further comprises information indicating whether thefirst device supports joint optimization of two or more components ofthe at least one air interface component.Example Embodiment 130. The apparatus of any of Example Embodiments 124to 129, wherein receiving the information regarding an AI/ML capabilityof the first device comprises receiving the information as part of aninitial network access procedure for the first device.Example Embodiment 131. The apparatus of any of Example Embodiments 124to 130, wherein transmitting the AI/ML training request comprisestransmitting the AI/ML training request through downlink controlinformation (DCI) on a downlink control channel or RRC signaling or thecombination of the DCI and RRC signaling.Example Embodiment 132. The apparatus of Example Embodiment 131, whereinthe receiving module is configured to receive a training requestresponse from the device confirming that the device has transitioned toan AI/ML training mode.Example Embodiment 133. The apparatus of any of Example Embodiments 124to 132, wherein the transmitting module is configured to transmit atraining signal to the first device, the training signal including atraining sequence or training data for training at least one AI/MLmodule responsible for one or more components of the at least one airinterface component.Example Embodiment 134. The apparatus of Example Embodiment 133, whereintransmitting the training signal comprises transmitting the trainingsignal on a dynamic control channel.Example Embodiment 135. The apparatus of Example Embodiment 134, whereinthe dynamic control channel includes a dynamic control information (DCI)field containing information indicating an AI/ML module that is to betrained.Example Embodiment 136. The apparatus of Example Embodiment 133, whereintransmitting the training signal comprises transmitting the trainingsignal on a scheduled data channel.Example Embodiment 137. The apparatus of Example Embodiment 136, whereinthe transmitting module is configured to transmit scheduling informationfor the data channel on a dynamic control channel that includes a DCIfield containing information indicating an AI/ML module that is to betrained.Example Embodiment 138. The apparatus of any of Example Embodiments 133to 137, wherein the receiving module is configured to receive a trainingresponse message from the first device, the training response messageincluding feedback information based on processing of the receivedtraining signal at the first device.Example Embodiment 139. The apparatus of Example Embodiment 138, whereinthe feedback information included in the training response messageincludes an updated training sequence for an iterative training process.Example Embodiment 140. The apparatus of Example Embodiment 138 or 139,wherein the feedback information included in the training responsemessage includes measurement results based on the received trainingsignal.Example Embodiment 141. The apparatus of Example Embodiment 140, whereinthe measurement results include an error margin obtained by the firstdevice in receiving the training signal.Example Embodiment 142. The apparatus of any of Example Embodiments 138to 141, further comprising a processing module configured to train oneor more AI/ML modules based on the feedback information provided in thetraining response message from the first device.Example Embodiment 143. The apparatus of Example Embodiment 142, whereinthe transmitting module is configured to:

transmit AI/ML update information to the first device, the AI/ML updateinformation including information indicating updated AI/ML parametersfor at least one of the one or more AI/ML modules based on the training.

Example Embodiment 144. The apparatus of any of Example Embodiments 133to 143, wherein the receiving module is configured to:

receive AI/ML update information from the first device, the AI/ML updateinformation from the first device including information indicatingupdated AI/ML parameters for at least one of the one or more AI/MLmodules based on training of one or more AI/ML modules at the firstdevice based on the training signal.

Example Embodiment 145. The apparatus of Example Embodiment 144, furthercomprising a processing module configured to update the at least one airinterface component for transmitting to the first device by updating theone or more AI/ML modules in accordance with the updated AI/MLparameters transmitted to the first device and the updated AI/MLparameters received from the first device.Example Embodiment 146. The apparatus of any of Example Embodiments 124to 145, wherein the transmitting module is configured to transmit atraining termination signal to the first device to indicate that atraining phase has finished.Example Embodiment 147. The apparatus of any of Example Embodiments 124to 146, wherein the first device is user equipment and the second deviceis a network device.

Although the present disclosure describes methods and processes withsteps in a certain order, one or more steps of the methods and processesmay be omitted or altered as appropriate. One or more steps may takeplace in an order other than that in which they are described, asappropriate.

Although the present disclosure is described, at least in part, in termsof methods, a person of ordinary skill in the art will understand thatthe present disclosure is also directed to the various components forperforming at least some of the aspects and features of the describedmethods, be it by way of hardware components, software or anycombination of the two. Accordingly, the technical solution of thepresent disclosure may be embodied in the form of a software product. Asuitable software product may be stored in a pre-recorded storage deviceor other similar non-volatile or non-transitory computer readablemedium, including DVDs, CD-ROMs, USB flash disk, a removable hard disk,or other storage media, for example. The software product includesinstructions tangibly stored thereon that enable a processing device(e.g., a personal computer, a server, or a network device) to executeexamples of the methods disclosed herein. The machine-executableinstructions may be in the form of code sequences, configurationinformation, or other data, which, when executed, cause a machine (e.g.,a processor or other processing device) to perform steps in a methodaccording to examples of the present disclosure.

The present disclosure may be embodied in other specific forms withoutdeparting from the subject matter of the claims. The described exampleembodiments are to be considered in all respects as being onlyillustrative and not restrictive. Selected features from one or more ofthe above-described embodiments may be combined to create alternativeembodiments not explicitly described, features suitable for suchcombinations being understood within the scope of this disclosure.

All values and sub-ranges within disclosed ranges are also disclosed.Also, although the systems, devices and processes disclosed and shownherein may comprise a specific number of elements/components, thesystems, devices and assemblies could be modified to include additionalor fewer of such elements/components. For example, although any of theelements/components disclosed may be referenced as being singular, theembodiments disclosed herein could be modified to include a plurality ofsuch elements/components. The subject matter described herein intends tocover and embrace all suitable changes in technology.

1. A method in a wireless communication network, the method comprising:transmitting, by a first device, information regarding an artificialintelligence or machine learning (AI/ML) capability of the first deviceto a second device over an air interface between the first device andthe second device, the information regarding an AI/ML capability of thefirst device identifying whether the first device supports AI/ML foroptimization of at least one air interface component over the airinterface.
 2. The method of claim 1, wherein the information regardingan AI/ML capability of the first device comprises information indicatingat least one of the following: the first device is capable of supportinga type and/or level of complexity of AI/ML; whether the first deviceassists with an AI/ML training process for optimization of the at leastone air interface component; at least one component of the at least oneair interface component for which the first device supports AI/MLoptimization.
 3. The method of claim 2, wherein the at least onecomponent of the at least one air interface component includes at leastone of a coding component, a modulation component and a waveformcomponent.
 4. The method of claim 2, wherein the information indicatingat least one component of the at least one air interface component forwhich the first device supports AI/ML optimization further comprisesinformation indicating whether the first device supports jointoptimization of two or more air interface components.
 5. The method ofclaim 1, wherein transmitting the information regarding an AI/MLcapability of the first device comprises at least one of: transmittingthe information in response to receiving an enquiry; and transmittingthe information as part of an initial network access procedure.
 6. Amethod in a wireless communication network, the method comprising:receiving, by a second device, information regarding an artificialintelligence or machine learning (AI/ML) capability of a first deviceover an air interface between the first device and the second device,the information regarding an AI/ML capability of the first deviceidentifying whether the first device supports AI/ML for optimization ofat least one air interface component over the air interface; andtransmitting an AI/ML training request to the first device based atleast in part on the information regarding the AI/ML capability of thefirst device.
 7. The method of claim 6, wherein the informationregarding an AI/ML capability of the first device comprises informationindicating at least one of the following: the first device is capable ofsupporting a type and/or level of complexity of AI/ML; whether the firstdevice assists with an AI/ML training process for optimization of the atleast one air interface component; at least one component of the atleast one air interface component for which the first device supportsAI/ML optimization.
 8. The method of claim 7, wherein the at least onecomponent of the at least one air interface component includes at leastone of a coding component, a modulation component and a waveformcomponent.
 9. The method of claim 7, wherein the information indicatingat least one component of the at least one air interface component forwhich the first device supports AI/ML optimization further comprisesinformation indicating whether the first device supports jointoptimization of two or more components of the at least one air interfacecomponent.
 10. The method of claim 6, wherein receiving the informationregarding an AI/ML capability of the first device comprises receivingthe information as part of an initial network access procedure for thefirst device.
 11. The method of claim 6, wherein transmitting the AI/MLtraining request comprises transmitting the AI/ML training requestthrough downlink control information (DCI) on a downlink control channelor RRC signaling or the combination of the DCI and RRC signaling. 12.The method of claim 11, further comprising, receiving a training requestresponse from the device confirming that the device has transitioned toan AI/ML training mode.
 13. The method of claim 6, further comprising:transmitting a training termination signal to the first device toindicate that a training phase has finished.
 14. An apparatuscomprising: at least one processor; and a computer readable storagemedium operatively coupled to the at least one processor, the computerreadable storage medium storing programming for execution by the atleast one processor, the programming comprising instructions to:transmit, from the apparatus, information regarding an artificialintelligence or machine learning (AI/ML) capability of the apparatus toa network device over an air interface between the appatus and thenetwork device, the information regarding an AI/ML capability of theapparatus identifying whether the apparatus supports AI/ML foroptimization of at least one air interface component over the airinterface.
 15. The apparatus of claim 14, wherein the informationregarding an AI/ML capability of the apparatus comprises informationindicating at least one of the following: the apparatus is capable ofsupporting a type and/or level of complexity of AI/ML; whether theapparatus assists with an AI/ML training process for optimization of theat least one air interface component; at least one component of the atleast one air interface component for which the apparatus supports AI/MLoptimization.
 16. The apparatus of claim 15, wherein the at least onecomponent of the at least one air interface component includes at leastone of a coding component, a modulation component and a waveformcomponent.
 17. The apparatus of claim 15, wherein the informationindicating at least one component of the at least one air interfacecomponent for which the apparatus supports AI/ML optimization furthercomprises information indicating whether the apparatus supports jointoptimization of two or more components of the at least one air interfacecomponent.
 18. A network apparatus comprising: at least one processor;and a computer readable storage medium operatively coupled to the atleast processor, the computer readable storage medium storingprogramming for execution by the at least processor, the programmingcomprising instructions to: receive, by the network apparatusinformation regarding an artificial intelligence or machine learning(AI/ML) capability of a first device over an air interface between thefirst device and the network apparatus, the information regarding anAI/ML capability of the first device identifying whether the firstdevice supports AI/ML for optimization of at least one air interfacecomponent over the air interface; and transmit an AI/ML training requestto the first device based at least in part on the information regardingthe AI/ML capability of the first device.
 19. The network apparatus ofclaim 18, wherein the information regarding an AI/ML capability of thefirst device comprises information indicating at least one of thefollowing: the first device is capable of supporting a type and/or levelof complexity of AI/ML; whether the first device assists with an AI/MLtraining process for optimization of the at least one air interfacecomponent; at least one component of the at least one air interfacecomponent for which the first device supports AI/ML optimization. 20.The network apparatus of claim 19, wherein the at least one component ofthe at least one air interface component includes at least one of acoding component, a modulation component and a waveform component. 21.The network apparatus of claim 19, wherein the information indicating atleast one component of the at least one air interface component forwhich the first device supports AI/ML optimization further comprisesinformation indicating whether the first device supports jointoptimization of two or more components of the at least one air interfacecomponent.